Susquehanna-Roseland FINAL REPORT

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Economic Impacts on New Jersey of Upgrading PSE&G’s Susquehanna-Roseland Transmission System Dr. Joseph J. Seneca Dr. Michael L. Lahr Dr. James W. Hughes Will Irving May 2009

Transcript of Susquehanna-Roseland FINAL REPORT

Economic Impacts on New Jersey of Upgrading PSE&G’s

Susquehanna-Roseland Transmission System

Dr. Joseph J. Seneca

Dr. Michael L. Lahr

Dr. James W. Hughes

Will Irving

May 2009

Table of Contents

EXECUTIVE SUMMARY ........................................................................................................ i

INTRODUCTION................................................................................................................... 1

Project Background...................................................................................................... 1

Analytical Approach..................................................................................................... 1

Organization of the Report .......................................................................................... 2

ECONOMIC IMPACT ANALYSIS .......................................................................................... 3

Expenditures Considered in the Analysis................................................................... 3

R/ECON™ Input-Output Model................................................................................. 3

Transmission Line and Towers (Monopole Structures)............................................ 4

Expenditure Assumptions ............................................................................................ 4

Economic Impacts ....................................................................................................... 6

Transmission Line and Towers (Lattice Structures)............................................... 11

Expenditure Assumptions .......................................................................................... 11

Economic Impacts ..................................................................................................... 13

East Hanover/Roseland Switching Station............................................................... 17

Expenditure Assumptions .......................................................................................... 17

Economic Impacts ..................................................................................................... 18

Jefferson Switching Station........................................................................................ 22

Expenditure Assumptions .......................................................................................... 22

Economic Impacts ..................................................................................................... 23

Combined Economic Impacts (Monopole Towers).................................................. 27

Combined Economic Impacts (Lattice Towers)....................................................... 30

CONCLUSION .................................................................................................................... 33

APPENDIX A: ECONOMIC AND DEMOGRAPHIC PROFILES AND DYNAMICS.................... 34

APPENDIX B: INPUT-OUTPUT ANALYSIS ......................................................................... 53

APPENDIX C: ECONOMIC IMPACTS OF COMBINED LATTICE-MONOPOLE SCENARIO... 74

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EXECUTIVE SUMMARY

This report presents the estimated economic impacts in New Jersey of the

approximately $649 - $750 million in expenditures required for construction of the New

Jersey portion of the proposed upgrade to PSE&G’s Susquehanna-Roseland

Transmission Network. The economic impacts estimated are only those associated with

the expenditures to be made on construction of the network upgrade, and do not reflect

any of the potential ongoing economic impacts of the increased transmission capacity

once the upgrade is complete.

The proposed upgrade would add 500 kV of additional power transmission

capacity to the existing 230 kV network. This analysis examines the economic impacts

of the construction of two switching stations and of the transmission line and towers

required to accommodate the increased transmission capacity. Alternative scenarios are

presented to reflect the two different types of tower structures that may be used. If all

249 towers were lattice structures, the estimated total expenditures for the project would

be approximately $649 million, whereas if all the towers were monopole structures, the

estimated expenditures would total $750 million. (Appendix C at the end of the report

provides the aggregate expenditures and economic impacts for a 50%-50% split between

the two types of towers.)

The estimated economic impacts include both direct impacts and indirect impacts.

Direct impacts are those directly associated with the project expenditures, such as the

construction employment required for the project and purchases of material to be used in

construction of the switching station and towers. Indirect impacts are those generated by

the multiplier effects of the initial expenditures, as the salaries paid to workers and the

business revenue generated by the expenditures made on materials in New Jersey are then

re-spent throughout the economy, generating further economic activity and impacts in the

form of employment, gross domestic product, compensation (income) and tax revenues.

Based on the two expenditure scenarios associated with the different types of

towers and on the associated range of project expenditures to be made in New Jersey, the

following economic impacts were estimated:

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• Employment. It is estimated that construction of the switching stations and

transmission line and towers will generate from 3,415 to 3,931 total job-years

(one job-year is equal to one job lasting one year). This includes from 2,258 to

2,646 direct job-years, including construction employment, as well as design

work, consulting services and other

• Gross Domestic Product. It is estimated that the construction of the upgrade will

generate between $396.1 and $428.1 million in gross domestic product for New

Jersey.

• Compensation. It is estimated that the total compensation generated by both the

direct and the indirect employment generated by the construction of the upgraded

network will be between $307.5 million and $333.8 million.

• State Tax Revenues. It is estimated that the construction phase of the project will

generate between $8 and $9 million in state taxes.

• Local Tax Revenues. It is estimated that the construction phase of the project will

generate between $7.9 and $9.9 million in local taxes.

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INTRODUCTION

This report presents the findings of an economic impact analysis of the

approximately $649-$750 million in expenditures required for construction of the New

Jersey portion of the proposed upgrade to PSE&G’s Susquehanna-Roseland

Transmission Network.

Project Background

PJM Interconnection, the regional authority overseeing electricity transmission in

all or part of 13 states, including New Jersey and Pennsylvania, has determined that the

existing 230 kV capacity of the transmission line running from Susquehanna,

Pennsylvania to Roseland, New Jersey is not sufficient to accommodate projected

demand growth in coming years. As a result, PJM has directed PSE&G of New Jersey

and PPL of Pennsylvania to upgrade the network by adding a new 500 kV capacity

transmission line to the existing network. The upgrade will require not only the addition

of the new power line itself, but also construction of new towers to accommodate both

the new 500 kV, as well as the existing 230 kV line, and the construction of two new

switching stations along the transmission route, one in Jefferson, New Jersey and one

near the line’s terminus in Roseland, New Jersey. This economic impact analysis covers

the estimated $649-$750 million of expenditures required for construction of the New

Jersey portion of the new line, including the two switching stations to be built in New

Jersey.

Analytical Approach

The economic impacts of the construction of the new transmission line in New

Jersey are estimated using the R/ECON™ Input-Output model developed and maintained

by the Center for Urban Policy Research at Rutgers University’s Edward J. Bloustein

School of Planning and Public Policy. The model provides estimates of a broad and

detailed range of economic impacts, including employment, gross domestic product,

income and tax revenues. A detailed description of the model and its methodology is

provided with the analysis.

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The construction of each component of the new transmission infrastructure is

analyzed individually. That is, the transmission line and towers are analyzed separately,

as are each of the two switching stations. In addition, two separate analyses of the

transmission line and tower infrastructure are provided. Because the existing towers

currently carrying the 230 kV line do not meet the specifications required to handle two

lines of the given capacities discussed, an additional 249 new towers are required in New

Jersey. Some of these towers will be monopole structures (i.e., a single pole with

branches holding the transmission lines), while others will be wider lattice-type

structures. Because each of these tower types requires a different mix of material and

labor inputs, two separate analyses are provided, one assuming that all towers are

monopole, and the other assuming that all towers are lattice.

Organization of the Report

The report begins with a brief overview of the expenditures considered in the

analysis. This is followed by a description of the R/ECON™ Input-Output Model and its

application. Next, the analyses of the separate components of the transmission network

are presented. These analyses consist of the all-monopole transmission line and towers,

the all-lattice transmission line and towers, the Jefferson Switching Station, and the

Roseland/East Hanover Switching Station. Each analysis consists of a review of the

input expenditures used in the R/ECON™ Input-Output Model and a detailed

presentation of the estimated economic impacts of those expenditures. A final section

presents the combined impacts of the total investment in New Jersey for both the all-

monopole and all-lattice tower scenarios. This is followed by a brief summary and

conclusions. An appendix presents a brief economic profile of the areas in New Jersey

where the new transmission line would be built.

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ECONOMIC IMPACT ANALYSIS

Expenditures Considered in the Analysis

Because of the highly specialized nature of the power transmission materials and

equipment needed for construction of the upgraded network, almost all the required

material will be purchased outside of New Jersey. As such, the majority of the impacts

measured in this analysis are generated via the employment of construction workers and

the purchase of specialized services associated with the project. The majority of these

workers and services are expected to come from New Jersey. Detailed explanations of

the specific expenditures made in New Jersey are provided for each component of the

analysis.

R/ECON™ Input-Output Model

The R/ECON™ Input-Output Model at the Center for Urban Policy Research at

the Bloustein School of Planning and Public Policy was used to measure the economic

impacts of the proposed expenditures for the Susquehanna-Roseland network upgrade.

The R/ECON™ model consists of 515 individual sectors of the New Jersey economy and

measures the effect of changes in expenditures in one industry on economic activity in all

other industries. Thus, the expenditures made on labor, materials, legal and design

services, and other inputs for the transmission line have both direct economic effects as

those expenditures become incomes and revenues for workers and businesses, and

subsequent indirect effects as those workers and businesses, in turn, spend those dollars

on other things – consumer goods, business investment expenditures, which, in turn,

become income for other workers and businesses. This income gets further spent, and so

on.

In summary, the R/ECON™ Input-Output model estimates both the direct

economic effects of the initial expenditures (in terms of jobs and income) and the indirect

(or multiplier) effects (in additional jobs and income) of the subsequent economic activity

that occurs following the initial expenditures. The model also estimates the gross

domestic product for New Jersey and the tax revenues generated by the combined direct

and indirect new economic activity caused by the initial spending.

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Transmission Line and Towers (Monopole Structures)

Expenditure Assumptions

This estimate of the economic impacts for construction of the transmission line

and towers for the Susquehanna-Roseland network assumes that all towers are monopole

structures.1 In order to reflect the full scope of the expenditures included in PSE&G’s

cost estimates for construction of the transmission line and towers portion of the

upgraded network, it was necessary to make several assumptions and adjustments to the

various expenditure items included in PSE&G’s initial cost estimates. Following is an

explanation of this process.

PSE&G’s estimated total cost for construction of the transmission line and

monopole towers is $497.9 million.

The base cost of construction estimated by PSE&G for the all-monopole

transmission line and towers, including labor, materials, third party

professional services and PSE&G support, was $380.4 million, with an

additional 11% in estimated inflation costs (“escalation”) and an

additional 20% in contingency.

In order to incorporate all potential expenditures into the analysis, the

escalation (11%) and contingency (20%) estimates were distributed

proportionately between the costs of labor and material and the other

costs (professional services, PSE&G support, etc.) according to their

respective shares of the $380.4 million base cost.

In addition, the OH&P on labor (25% of base labor and material costs) and

material (10% of base labor and material costs), the Scope Modifications

on labor (15% of base labor and material costs) and material (15% of base

1 Scenarios assuming all monopole and all lattice tower structures are presented in the body of the report. A spreadsheet indicating the aggregate impacts of using 50% of each type of tower structure is presented in Appendix C.

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labor and material costs), and the Inefficiencies on labor (18% of base

labor and material costs) were also distributed proportionately across the

labor and material components of the base construction cost structure.

The separate expenditures on labor and materials for the laying of tower

foundations were not broken out in PSE&G’s cost specifications. For

purposes of the analysis, 65% of the $59.4 million in expenditures on

foundations was allocated to labor, and the remaining 35% to material.

These various adjustments resulted in total allocations of $247.8 million

for transmission line construction labor and $171.9 million for material.

All direct construction labor was assumed to come from North Jersey.

All specialty materials (conductors, insulators, field wire, tower structures,

etc.) are assumed to be purchased from outside of New Jersey. As such, of

the material expenditures, only the concrete and other material used for

construction of the tower foundations was incorporated into the impact

estimate.

Of the “Other Costs” (i.e., professional services, PSE&G support, etc.)

associated with construction of the transmission line, the costs for

consulting services provided by Louis Berger, the cost of soil borings, the

costs of appraisals, title and mapping costs, and the costs of PSE&G legal

fees were incorporated into the economic impact estimate. These

expenditures totaled approximately $8 million.

In addition, PSE&G’s support costs were allocated according to the shares

reflected in the itemized cost breakdowns of PSE&G support in the cost

estimates for the two switching stations. All of these costs were

incorporated into the economic impact estimate, with the exception of

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“Licensing/Permits/Bonds/Builder’s Risk.” Of this last category,

approximately $3.8 million in builder’s risk insurance was incorporated

into the expenditure estimates.

As a result of these assumptions and adjustments, $337.5 million of the

total $497.9 million estimate for construction of the transmission line and

towers (or 67.8%) was allocated to expenditures on labor, material and

services in New Jersey.

Economic Impacts

Based on the R/Econ Input-Output model, Table 1 lists the estimates of the

economic impacts of the expenditures made in New Jersey for construction of the

Transmission Line and Towers

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 1,600 1,000 2,600 1.62 GDP ($ 000) 256,252 63,825 320,078 1.25Compensation ($ 000) 206,970 42,781 249,751 1.21 State Tax Revenue ($000) - - 6,943 - Local Tax Revenue ($000) - - 5,332 -

As noted in the preceding section, these impacts are based on estimated in-state

expenditures on labor, material and services of approximately $337.5 million. They do

not reflect the impacts of the remaining $160.4 million in transmission line-related

expenditures to be made outside of New Jersey. Explanatory notes for each indicator

follow Table 2.

Table 2 lists estimates of the total employment generated in New Jersey by the

Transmission Line expenditures by business sector.

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Table 2

Sector Employment (job-years)

Natural Resources & Mining 16 Construction 1,185 Manufacturing 306 Transportation & Public Utilities 83 Wholesale Trade 101 Retail Trade 276 Financial Activities 159 Services 472 Government 2 Total 2,600

Indicator Explanations:

• Employment

Employment impacts are measured in job-years (i.e., one job lasting one year).

The all-monopole transmission line and towers component of the project is

estimated to generate a total of 2,600 jobs in New Jersey. Based on salary and

benefit estimates for the employment required to upgrade the transmission line,

approximately 1,185 direct construction jobs are estimated to be created. Note

also from Table 1 that the direct employment associated with the construction of

the transmission line (1,600 jobs) exceeds the total construction employment

(1,185 jobs) listed in Table 2. The additional direct employment (415 jobs)

associated with the project is generated in the New Jersey-based wholesale and

manufacturing sectors that produce and distribute the non-specialized materials

used in laying foundations, building access roads, etc., as well as the various

PSE&G internal functions associated with project management and support, and

outside services (e.g., legal) provided by New Jersey firms. Significant additional

indirect employment (1,000 jobs, Table 1) is generated across various sectors,

including services, financial activities, and retail trade.

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• GDP

Note that the total GDP generated in the state ($320.1 million, Table 1) is close to

the total expenditures estimated for New Jersey. By explicitly excluding those

material expenditures that are to be made outside of New Jersey, the model

minimizes the economic “leakage” that would normally be reflected were they to

have been included. That is, were the excluded $160.4 million in expenditures to

be included, the relative proportion of impacts leaked from the New Jersey

economy would be higher. This leakage is reflected in the per-million dollar

impacts reported below.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The transmission line and monopole towers

component is estimated to generate $249.8 million in compensation in New

Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales associated with the economic output generated by the project. The

transmission line and monopole towers component is estimated to generate $6.9

million in state tax revenues.

• Local Tax Revenues

The estimates of the increase in local tax revenues are for the entire state. The

increase represents a long-run estimate of property tax revenues generated by

payment of residential and commercial property taxes from the personal and

business incomes generated by the project and/or resulting from improvements

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made to property caused by the increased economic activity generated by the

project.

Local tax revenues increase because the additional economic activity from the

transmission line project generates income for workers and revenues for

business2. The increases in personal incomes and in business revenues are, in

part, used to pay property taxes and to improve properties (both residential and

commercial). Thus, households benefitting from the additional jobs and resulting

incomes acquire and/or improve residential properties, and are able to pay rents

and mortgages and the associated property taxes. Similarly, business income and

profits also increase as a direct result of higher sales and output caused by the

project. Businesses subsequently acquire and/or improve their properties.

Historical New Jersey fiscal and economic data are used to measure the

relationship between business revenues and the amount of commercial property

tax revenues collected, and between household incomes and the amount of

residential property tax revenues collected.3 Given the increases in both

household income and the business revenues caused by the expenditures made on

the transmission line, the R/ECON™ Input-Output Model invokes the known

statistical relation of local property tax revenues to both household income and

business revenues in order to estimate the addition to local tax revenues

attributable to the transmission line project.

It is important to note that this additional tax revenue occurs over a period of time.

It is not an immediately generated impact. The economic sequence is as follows.

The additions/improvements to residential and commercial property financed by

the higher household incomes and higher business revenues are, in time, captured

by higher property assessments, which, in turn, generate higher local tax

2 For businesses, the revenue increase is measured in terms of value-added, and it is the change in value-added in the business sector that is the basis for the estimated change in property tax revenues. 3 For the entire state, approximately 76% of total local property tax revenues are attributable to residential property; with approximately 21% derived primarily from commercial and industrial property.

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revenues. There are time lags between the increase in incomes and revenues, the

improvements to property, and the increase in assessed values. Thus, the local

tax revenue impacts estimated in this analysis are the outcome of a long-run

adjustment process. This process occurs over the entire state based on the

geographical dispersal within New Jersey of the households and businesses that

benefit from the expenditures on the transmission line.

Table 3 provides the per-million dollar spending impacts for the transmission

line. Note that these impacts are calculated per million dollars of total transmission line

expenditures – that is, on the basis of the $497.9 million to be spent both inside and

outside of New Jersey.

Table 3

Indicator Impacts per $1 million of total project expenditures

Employment (job-years) 5.2 GDP $642,864 Income $501,616 State Tax Revenues $13,944 Local Tax Revenues $10,709

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Transmission Line and Towers (Lattice Structures)

Expenditure Assumptions

This estimate of the economic impacts for construction of the transmission line

and towers for the Susquehanna-Roseland network assumes that all towers are lattice

structures. In order to reflect the full scope of the expenditures included in PSE&G’s cost

estimates for construction of the transmission line and towers portion of the upgraded

network, it was necessary to make several assumptions and adjustments to the various

expenditure items included in PSE&G’s initial cost estimates. Following is an

explanation of this process.

PSE&G’s estimated total cost for construction of the transmission line and

lattice towers is $397.1 million.

The base cost of construction estimated by PSE&G for the all-lattice

transmission line and towers, including labor, materials, third party

professional services and PSE&G support, was $303.2 million, with an

additional 11% in estimated inflation costs (“escalation”) and an

additional 20% in contingency.

In order to incorporate all potential expenditures into the analysis, the

escalation (11%) and contingency (20%) estimates were distributed

proportionately between the costs of labor and material and the other

costs (professional services, PSE&G support, etc.) according to their

respective shares of the $303.2 million base cost.

In addition, the OH&P on labor (25% of base labor and material costs) and

material (10% of base labor and material costs), the Scope Modifications

on labor (15% of base labor and material costs) and material (15% of base

labor and material costs), and the Inefficiencies on labor (18% of base

labor and material costs) were also distributed proportionately across the

labor and material components of the base construction cost structure.

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The separate expenditures on labor and materials for the laying of tower

foundations were not disaggregated in PSE&G’s cost specifications. For

purposes of the analysis, 65% of the $20.2 million in expenditures on

foundations was allocated to labor, and the remaining 35% to material.

These various adjustments resulted in total allocations of $239.2 million

for transmission line construction labor and $74.4 million for material.

All direct construction labor was assumed to come from North Jersey.

All specialty materials (conductors, insulators, field wire, tower structures,

etc.) are assumed to be purchased from outside of New Jersey. As such, of

the material expenditures, only the concrete and other material used for

construction of the tower foundations was incorporated into the impact

estimate.

Of the “Other Costs” (i.e., professional services, PSE&G support, etc.)

associated with construction of the transmission line, the costs for

consulting services provided by Louis Berger, the cost of soil borings, the

costs of appraisals, title and mapping costs, and the costs of PSE&G legal

fees were incorporated into the economic impact estimate. These

expenditures totaled approximately $8.1 million.

In addition, PSE&G’s support costs were allocated according to the shares

reflected in the itemized cost breakdowns of PSE&G support in the cost

estimates for the two switching stations. All of these costs were

incorporated into the economic impact estimate, with the exception of

“Licensing/Permits/Bonds/Builder’s Risk.” Of this last category,

approximately $3.1 million in builder’s risk insurance was incorporated

into the expenditure estimates.

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As a result of these assumptions and adjustments, $292.3 million of the

total $397.1 million estimate for construction of the transmission line and

towers (or 73.6%) was allocated to expenditures on labor, material and

services in New Jersey.

Economic Impacts

Based on the R/ECON™ Input-Output model, Table 1 lists the estimates of the

economic impacts of the expenditures made in New Jersey for construction of the all-

lattice transmission line and towers

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 1,212 872 2,084 1.72 GDP ($ 000) 233,318 54,787 288,104 1.24Compensation ($ 000) 186,827 36,650 223,477 1.20 State Tax Revenue ($000) - - 5,932 - Local Tax Revenue ($000) - - 7,329 -

As noted in the preceding section, these impacts are based on estimated in-state

expenditures on labor, material and services of approximately $292.3 million. They do

not reflect the impacts of the remaining $104.8 million in lattice-tower transmission line-

related expenditures to be made outside of New Jersey. Explanatory notes for each

indicator follow Table 2.

Table 2 lists estimates of the total employment generated in New Jersey by the

all-lattice structure transmission line expenditures by business sector.

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Table 2

Sector Employment (job-years)

Natural Resources & Mining 7 Construction 996 Manufacturing 155 Transportation & Public Utilities 72 Wholesale Trade 53 Retail Trade 249 Financial Activities 140 Services 412 Government 0 Total 2,084

Indicator Explanations:

• Employment

Employment impacts are measured in job-years (i.e., one job lasting one year).

The all-lattice transmission line and towers component of the project is estimated

to generate a total of 2,084 jobs in New Jersey. Based on salary and benefit

estimates for the employment required to upgrade the transmission line,

approximately 996 direct construction jobs are estimated to be created. Note also

from Table 1 that the direct employment associated with the construction of the

transmission line (1,212 jobs) exceeds the total construction employment (996

jobs) listed in Table 2. The additional direct employment (216 jobs) associated

with the project is generated in the New Jersey-based wholesale and

manufacturing sectors that produce and distribute the non-specialized materials

used in laying foundations, building access roads, etc., as well as the various

PSE&G internal functions associated with project management and support, and

outside services (e.g., legal) provided by New Jersey firms. Significant additional

indirect employment (872 jobs, Table 1) is generated across various sectors,

including services, financial activities, and retail trade.

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• GDP

Note that the total GDP generated in the state ($288.1 million, Table 1) is close to

the total expenditures estimated for New Jersey. By explicitly excluding those

material expenditures that are to be made outside of New Jersey, the model

minimizes the economic “leakage” that would normally be reflected were they to

have been included. That is, were the excluded $104.8 million in expenditures to

be included, the relative proportion of impacts leaked from the New Jersey

economy would be higher. It is important to note that this leakage is reflected in

the per-million dollar impacts reported below.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The transmission line and lattice towers

component is estimated to generate $223.5 million in compensation in New

Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales associated with the economic output generated by the project. The

transmission line and lattice towers component is estimated to generate $5.9

million in state tax revenues.

• Local Tax Revenues

Local tax revenues are comprised of increased property tax revenues resulting

from improvements associated with the increased business activity generated by

the project. The transmission line and lattice towers component is estimated to

generate $7.3 million in local tax revenues.

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Table 3 provides the per-million dollar spending impacts for the transmission

line. Note that these impacts are calculated per million dollars of total transmission line

expenditures – that is, on the basis of the $373.2 million to be spent both inside and

outside of New Jersey.

Table 3

Indicator Impacts per $1 million of total project expenditures

Employment (job-years) 5.2 GDP $725,533 Income $562,797 State Tax Revenues $14,940 Local Tax Revenues $18,457

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East Hanover/Roseland Switching Station

Expenditure Assumptions

In order to reflect the full range of expenditures incorporated into PSE&G’s cost

estimates for construction of the East Hanover/Roseland switching station portion of the

upgraded Susquehanna-Roseland network, the following assumptions and adjustments

were made to the various construction expenditures.

The total cost of construction for the East Hanover/Roseland switching

station was estimated at $166.6 million, including $125.6 million in base

costs and $41 million in contingency.

The contingency and escalation (32.6%) estimates were distributed

proportionately between the contractor’s labor and material costs, the

professional services costs, and the PSE&G support costs according to

their respective shares of the $125.6 million base cost.

Expenditures on transformers, circuit breakers, disconnect switches, and

other electronic equipment were assumed to be made outside of New

Jersey.

All direct construction labor was assumed to come from North Jersey.

As a result of these assumptions and adjustments, $57.1 million of the

total $166.6 million estimate for construction (or 34.3%) of the East

Hanover/Roseland Switching Station, was allocated to expenditures on

labor, material and services in New Jersey.

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Economic Impacts

Table 1 shows the economic impacts of the East Hanover/Roseland switching

station expenditures described above.

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 462 130 592 1.28 GDP ($ 000) 42,267 8,847 51,115 1.21Compensation ($ 000) 33,822 5,954 39,776 1.18 State Tax Revenue ($000) - - 968 - Local Tax Revenue ($000) - - 1,200 -

As noted in the preceding section, these impacts are based on estimated in-state

expenditures on labor and material of approximately $57.1 million. They do not reflect

the impacts of the remaining $109.5million in expenditures to be made outside of New

Jersey. Explanatory notes regarding each indicator follow Table 2.

Table 2 shows the total employment generated in New Jersey by the East

Hanover/ Roseland switching station expenditures by business sector.

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Table 2

Sector Employment (job-years)

Natural Resources & Mining 1 Construction 411 Manufacturing 27 Transportation & Public Utilities 12 Wholesale Trade 9 Retail Trade 29 Financial Activities 23 Services 79 Government 0 Total 592

• Employment

Employment impacts are measured in job-years (i.e., one job lasting one year).

The East Hanover/Roseland switching station portion of the project is estimated

to generate a total of 592 jobs in New Jersey. Based on salary and benefit

estimates for the employment required to construct the stations, approximately

411 direct construction jobs are estimated to be created. Note also from Table 1

that the direct employment associated with the construction of the switching

station (462 jobs) exceeds the total construction employment (411 jobs) listed in

Table 2. The additional direct employment (51 jobs) associated with the project is

generated in the New Jersey-based wholesale and manufacturing sectors that

produce and distribute the non-specialized materials used in laying foundations,

building access roads, etc., as well as the various PSE&G internal functions

associated with project management and support, and outside services (e.g., legal)

provided by New Jersey firms. Significant additional indirect employment (130

jobs, Table 1) is generated across various sectors, including services, financial

activities, and retail trade.

• GDP

As with the expenditures on construction of the transmission line, the GDP

generated in the state ($51.1 million) is close to the total expenditures estimated

for New Jersey due to the exclusion from the model of those material

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expenditures that are to be made outside of New Jersey. The per-million-dollar

impacts reported below reflect the impact on New Jersey per million dollars of

total expenditures, including those expenditures made outside of the state.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The construction of the East

Hanover/Roseland switching station is estimated to generate $39.8 million in

compensation in New Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales associated with the economic output generated by the project. The

construction of the East Hanover/ Roseland switching station is estimated to

generate $.968 million in state tax revenues.

• Local Tax Revenues

Local tax revenues are comprised of increased property tax revenues resulting

from improvements associated with the increased business activity generated by

the project. The construction of the East Hanover/Roseland switching station is

estimated to generate $1.120 million in local tax revenues.

Table 3 provides the per-million-dollar spending impacts for the East

Hanover/Roseland switching station. Note that these impacts are calculated per million

dollars of total expenditures – that is, on the basis of the $166.6 million to be spent both

inside and outside of New Jersey.

21

Table 3

Indicator Impacts per $1 million of total expenditures

Employment (job-years) 3.6 GDP 306,785 Compensation 238,730 State Tax Revenues 5,811 Local Tax Revenues 7,202

22

Jefferson Switching Station

Expenditure Assumptions

In order to reflect the full range of expenditures incorporated into PSE&G’s cost

estimates for construction of the Jefferson switching station portion of the upgraded

Susquehanna-Roseland network, it was necessary to make several assumptions and

adjustments to the various expenditure items listed for construction of the station.

Following is an explanation of this process.

The total cost of construction for the East Hanover/Roseland switching

station was estimated at $77 million, including $57.8 million in base costs,

$6.1 million in escalation costs and $13.1 million in contingency.

The escalation (10.5%) and contingency (22.7%) estimates were

distributed proportionately between the contractor’s labor and material

costs. The Professional Services costs and the PSE&G Support costs were

distributed according to their respective shares of the $57.8 million base

cost.

The Scope Assessment and Fees on the labor portion of the contractor’s

costs (34% of base costs) were combined with the labor costs.

Expenditures on circuit breakers, disconnect switches, and third party

professional services were assumed to be made outside of New Jersey.

Of the approximately $6.5 million in bulk material expenditures, 60% was

assumed to be electrical material, and 40% civil material, with 90% of the

electrical bulk being purchased outside New Jersey. The majority of civil

bulk material associated with site work, access roads, foundations, etc.,

was assumed to be purchased in New Jersey.

23

All direct construction labor was assumed to come from North Jersey.

As a result of these assumptions and adjustments, $62.1 million of the

total $77 million estimate for construction (or 80.5%) of the Jefferson

Switching Station was allocated to expenditures on labor, services and

material in New Jersey.

Economic Impacts

Table 1 shows the economic impacts of the Jefferson Switching Station

expenditures described above.

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 584 154 739 1.26 GDP ($ 000) 47,145 9,784 56,929 1.21Compensation ($ 000) 37,654 6,574 44,228 1.18 State Tax Revenue ($000) - - 1,080 - Local Tax Revenue ($000) - - 1,333 -

As noted in the preceding section, these impacts are based on estimated in-state

expenditures on labor and material of approximately $62.1 million. They do not reflect

the impacts of the remaining $14.9 million in expenditures to be made outside of New

Jersey. Explanatory notes regarding each indicator follow Table 2.

Table 2 shows the total employment generated in New Jersey by the Jefferson

Switching Station expenditures by business sector.

24

Table 2

Sector Employment (job-years)

Natural Resources & Mining 1 Construction 538 Manufacturing 26 Transportation & Public Utilities 12 Wholesale Trade 10 Retail Trade 47 Financial Activities 24 Services 80 Government 0 Total 739

• Employment

Employment impacts are measured in job-years (i.e., one job lasting one year).

The Jefferson Switching Station of the project is estimated to generate a total of

739 jobs in New Jersey. Based on salary and benefit estimates for the

employment required to construct the stations, approximately 538 direct

construction jobs are estimated to be created. Note also from Table 1 that the

direct employment associated with the construction of the Switching Stations

(584 jobs) exceeds the total construction employment (538 jobs) listed in Table 2.

The additional direct employment (46 jobs) associated with the project is

generated in the New Jersey-based wholesale and manufacturing sectors that

produce and distribute the non-specialized materials used in laying foundations,

building access roads, etc., as well as the various PSE&G internal functions

associated with project management and support, and outside services (e.g., legal)

provided by New Jersey firms. Significant additional indirect employment (154

jobs, Table 1) is generated across various sectors, including services, financial

activities, and retail trade.

• GDP

As with the expenditures on construction of the transmission line, the GDP

generated in the state ($56.9 million) is close to the total expenditures estimated

for New Jersey due to the exclusion from the model of those material

25

expenditures that are to be made outside of New Jersey. The per-million-dollar

impacts reported below reflect the impact on New Jersey per million dollars of

total expenditures, including those expenditures made outside of the state.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The construction of the Jefferson Switching

Station is estimated to generate $44.2 million in compensation in New Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales associated with the economic output generated by the project. The

construction of the Jefferson Switching Station is estimated to generate $1.1

million in state tax revenues.

• Local Tax Revenues

Local tax revenues are comprised of increased property tax revenues resulting

from improvements associated with the increased business activity generated by

the project. The construction of the Jefferson Switching Station is estimated to

generate $1.3 million in local tax revenues.

Table 3 provides the per-million-dollar spending impacts for the Jefferson

Switching Station. Note that these impacts are calculated per million dollars of total

transmission line expenditures – that is, on the basis of the $77 million to be spent both

inside and outside of New Jersey.

26

Table 3

Indicator Impacts per $1 million of total expenditures

Employment (job-years) 9.6 GDP $739,340 Compensation $574,392 State Tax Revenues $14,022 Local Tax Revenues $17,315

Note that these per-million-dollar impacts are significantly higher than those of

the East Hanover and Roseland stations. This is largely due to the fact that $70 million

dollars of expenditures on transformers and circuit breakers for the East Hanover and

Roseland stations is assumed to be spent out of state, while there are no comparable

expenditures for the Jefferson switching station. Thus, there is less economic “leakage”

assumed as a proportion of the total costs of construction.

27

Combined Economic Impacts (Monopole Towers)

Following are the combined impacts for all components of the project, including

the transmission line and towers (assuming monopole towers) and all switching stations.

Table 1 shows the aggregate economic impacts of the entire $741.5 million construction

project (the total project budget is $750 million when the management reserve is

included). The total expenditures estimated to be made in New Jersey are $497.9 million

(or 66.1%)

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 2,646 1,284 3,931 1.49 GDP ($ 000) 345,664 82,456 428,122 1.24Compensation ($ 000) 278,446 55,309 333,755 1.20 State Tax Revenue ($000) - - 8,991 - Local Tax Revenue ($000) - - 7,865 -

As noted in the preceding section, these impacts are based on estimated in-state

expenditures on labor and material of approximately $456.7 million. They do not reflect

the impacts of the remaining $284.8 million in expenditures to be made outside of New

Jersey or the $8.5 million management reserve. Explanatory notes regarding each

indicator follow Table 2.

Table 2 shows the total employment generated in New Jersey by the total

combined project expenditures.

Table 2

Sector Employment (job-years)

Natural Resources & Mining 18 Construction 2,134 Manufacturing 359 Transportation & Public Utilities 107 Wholesale Trade 120 Retail Trade 352 Financial Activities 206 Services 631 Government 2 Total 3,931

28

• Employment

Total employment generated by the project is estimated at 3,931 jobs, with the

majority of those jobs occurring in the construction industry (2,143 jobs). Other

sectors with large direct and indirect job gains include the aggregate services

sector (631 jobs), the retail trade sector (352 jobs), and the manufacturing sector

(359 jobs).

• GDP

The GDP generated in the state ($428.1 million) is close to the total expenditures

estimated for New Jersey due to the exclusion from the model of those material

expenditures that are to be made outside of New Jersey. The per-million-dollar

impacts reported below reflect the impact on New Jersey per million dollars of

total expenditures, including those expenditures made outside of the state.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The project is estimated to generate $333.8

million in compensation in New Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales taxes associated with the economic output generated by the project.

The project is estimated to generate $9 million in state tax revenues.

• Local Tax Revenues

Local tax revenues are comprised of increased property tax revenues that are

generated over time because of property improvements associated with the

increased business activity generated by the project. The value of these property

29

improvements is, in time, included in assessments and hence in property tax

revenues. The project is estimated to generate $7.9 million in local tax revenues.

Table 3 provides the per-million-dollar spending impacts for the full project.

Note that these impacts are calculated per million dollars of total expenditures – that is,

on the basis of the $741.5 million to be spent both inside and outside of New Jersey and

including the additional $8.5 million management reserve.

Table 3

Indicator Impacts per $1 million of total expenditures

Employment (job-years) 5.2 GDP $570,829 Compensation $445,007 State Tax Revenues $11,988 Local Tax Revenues $10,487

30

Combined Economic Impacts (Lattice Towers)

Following are the combined impacts for all components of the project, including

the transmission line and towers (assuming lattice towers) and all switching stations.

Table 1 shows the aggregate economic impacts of the entire $640.7 million construction

project (the total project budget is $649.2 million when the management reserve is

included). The total expenditures estimated to be made in New Jersey are $411.5 million

(or 64.2%)

Table 1

Indicator Direct Indirect Total Multiplier Employment (job-years) 2,258 1,156 3,415 1.51 GDP ($ 000) 322,730 73,418 396,148 1.23Compensation ($ 000) 258,303 49,178 307,481 1.19 State Tax Revenue ($000) - - 7,980 - Local Tax Revenue ($000) - - 9,862 -

As noted previously, these impacts are based on estimated in-state expenditures

on labor and material of approximately $411.5 million. They do not reflect the impacts of

the remaining $229.2 million in expenditures to be made outside of New Jersey.

Explanatory notes regarding each indicator follow Table 2.

Table 2 shows the total employment generated in New Jersey by the total

combined project expenditures.

Table 2

Sector Employment (job-years)

Natural Resources & Mining 9 Construction 1,945 Manufacturing 208 Transportation & Public Utilities 96 Wholesale Trade 72 Retail Trade 325 Financial Activities 187 Services 571 Government 0 Total 3,415

31

• Employment

Total employment generated by the project is estimated at 3,415 jobs, with the

majority of those generated in the construction industry (1,945 jobs). Other

sectors with large direct and indirect job gains include the aggregate services

sector (571 jobs), the retail trade sector (325 jobs), and the manufacturing sector

(208 jobs).

• GDP

The GDP generated in the state ($396.1 million) is close to the total expenditures

estimated for New Jersey due to the exclusion from the model of those material

expenditures that are to be made outside of New Jersey. The per-million-dollar

impacts reported below reflect the impact on New Jersey per million dollars of

total expenditures, including those expenditures made outside of the state.

• Compensation

Compensation represents the total wages, salaries and supplements to wages and

salaries (i.e., employer contributions to government and private pension funds)

paid for all direct and indirect jobs generated as a result of the project

expenditures made in New Jersey. The project is estimated to generate $307.5

million in compensation in New Jersey.

• State Tax Revenues

State tax revenues are comprised of the income taxes associated with the salaries

paid to the workers in the direct and indirect jobs associated with the project, and

with the sales taxes associated with the economic output generated by the project.

The project is estimated to generate $8 million in state tax revenues.

• Local Tax Revenues

Local tax revenues are comprised of increased property tax revenues that are

generated over time because of the improvements associated with the increased

business activity generated by the project. The value of these improvements is, in

32

time, included in assessments and hence in property taxes. The project is

estimated to generate $9.9 million in local tax revenues.

Table 3 provides the per-million-dollar spending impacts for the full project,

assuming all lattice tower structures are used for the transmission line. Note that these

impacts are calculated per million dollars of total expenditures – that is, on the basis of

the $640.7 million to be spent both inside and outside of New Jersey and including the

additional $8.5 million management reserve.

Table 3

Indicator Impacts per $1 million of total expenditures

Employment (job-years) 5.3 GDP $610,220 Compensation $473,639 State Tax Revenues $12,292 Local Tax Revenues $15,191

33

CONCLUSION

This report presents an economic impact analysis of the proposed upgrade of

PSE&G’s Susquehanna-Roseland transmission network. Using the Edward J. Bloustein

School’s R/ECON™ Input-Output model, impact estimates were generated for

construction of two switching stations and the transmission line and towers, including

separate analyses for two different types of tower. Based on the proposed employment

and other project expenditures to be made in New Jersey, we estimate that the $649.2

million (all lattice towers) to $750 million (all monopole towers) in project expenditures

($425.2 million to $467.7 million to be made in New Jersey), including management

reserves, will generate:

• between 3,415 (lattice) and 3,931 (monopole) job-years for workers in New

Jersey;

• between $396.1 million (lattice ) and $428.1 million (monopole) in gross

domestic product for the state;

• between $307.5 million (lattice) and $333.8 million in compensation for workers

in the jobs generated by the project in New Jersey;

• between $8 million (lattice) and $9 million (monopole) in state taxes; and

• between $7.9 million (monopole) and $9.9 million (lattice) in local taxes.

34

APPENDIX A: ECONOMIC AND DEMOGRAPHIC PROFILES AND DYNAMICS

The four counties where the transmission line work will occur – Essex, Morris,

Sussex, and Warren – together represent a microcosm of New Jersey, mirroring the

economic and demographic profile of the state, as well as the basic economic and

demographic dynamics of change. The four-county region has dense urban job and

residential concentrations, strong suburban job growth corridors and residential

communities, and dispersed rural-suburban territories characterized by very low density

development patterns.

The four-county economies are dominated by private service-providing

employment. The most significant service-providing sectors, in order of importance, are

trade, transportation, and utilities, professional and business services, and education and

health services. Employment growth rates in the four-county region have been relatively

modest, somewhat below those of the state, with education and health services the

leading growth sector.

Slow-growth demographics also characterize the four counties, with population

increases steadily declining through the decade. The principal reason for this slowdown

has been growing net migration losses – more people moving out than moving in – that

have now spread to even the low density counties of Sussex and Warren. The modest

population gains for the decade to date are due solely to net natural increase (births minus

deaths).

This Appendix examines the employment composition of the each of the counties

and the aggregated four-county region in the context of that of New Jersey as a whole, as

well as the patterns of change during the 2002-2008 period. This will be followed by a

demographic analysis of 2000-2008 period in terms of the basic components of

population change.

State, County, and Region Employment Analysis

The economies of Essex, Morris, Sussex, and Warren counties in total accounted

for nearly one-fifth (18.2 percent) of New Jersey’s total payroll employment in 2008

(730,495 jobs out of a state total of 4,007,911 jobs). Essex was the largest (363,038 jobs)

35

of the four counties, followed by Morris (289,095 jobs). Much smaller in size are Sussex

(40,407 jobs) and Warren (37,955 jobs). In general, Essex tends to have a concentrated

urban employment base (as well as a secondary suburban one), while that of Morris is

largely suburban highway-corridor oriented. Thus, Essex is the densest economy,

followed by Morris. In contrast, employment in Sussex has a much less dense and more

dispersed suburban-rural pattern. Warren is a mixture of urban, suburban, and rural, with

much of the county similar to Sussex. However, there is an older manufacturing base

centered in the area around Phillipsburg. Thus, the employment geography of the four-

county region is quite heterogeneous in terms of geographic distribution and density,

mirroring quite closely that of New Jersey in its entirety.

This is much less the case in the composition and growth patterns of each county.

The broad general job dynamic in the 2002-2008 period has been employment growth in

private-service providing activities and government, and employment declines in goods-

producing industries. The sector that dominated private-sector growth was education and

health services. This stands as a microcosm of what has occurred in the New Jersey

economy.

Employment Data

To analyze the four counties, New Jersey as a whole will serve as the baseline.

The source of both county and statewide data is the Quarterly Census of Employment and

Wages (QCEW) produced by the U.S. Bureau of Labor Statistics. At the state level, the

QCEW differs slightly from the Current Employment Survey (CES) payroll employment

series, which is sample-based and is released monthly by the New Jersey Department of

Labor and Workforce Development. The advantage of the QCEW is that it is a full count

and not a sample, and is available at the county level; its disadvantage is that the data are

not as current as the CES. The state data in the QCEW are the sum of the 21 counties.

The analysis begins in the second quarter of 2002 and ends in the second quarter

of 2008, the last quarter of data availability. The period of measurement was designed to

have the beginning and end points at similar stages in the business cycle. New Jersey’s

employment had peaked in December 2000, the end of the great economic expansion of

36

the 1990s. It then contracted through early 2003, when growth resumed. Employment

then kept expanding until it once again peaked, in December 2007, when the current

recession began. So both the beginning and end points (second quarter of 2002 and

second quarter of 2008) were in recessionary periods, with a modest economic expansion

in between.

Employment is classified into categories defined by the North American Industry

Classification System (NAICS). The initial partition is between the private sector and the

public sector (government). The private sector is further disaggregated into good-

producing industries and service-providing industries. There are three major goods-

producing industries – natural resources and mining, construction, and manufacturing.

Of the three, manufacturing is the largest.

There are seven major industries in the service-providing group: trade,

transportation, and utilities; information; financial activities; professional and business

services; education and health services; leisure and hospitality; and other services. The

three largest are trade, transportation, and utilities, professional and business services,

and educational and health services. The smallest is information. We will refer to them

as sectors or classifications. The following is a more detailed, but not complete,

definition of these sectors.

Trade, Transportation, and Utilities include wholesale trade, retail trade, transportation,

warehousing, and utilities.

Information includes publishing, telecommunications, Internet service providers, and

data processing activities.

Financial Activities include finance, insurance, and real estate.

Professional and Business Services includes professional, scientific, and technical

services; legal and accounting services; architectural and engineering services,

advertising, management of companies and enterprises, and administrative support.

37

Education and Health Services includes private education, health care, and social

assistance.

Leisure and Hospitality includes arts, entertainment, recreation, gambling, amusements,

accommodations, and food services and drinking places.

Other Services include repair and maintenance, personal and laundry services, and

religious, grant making, civic, professional and similar organizations.

The Baseline New Jersey Employment Growth Pattern

Between 2002 Q2 and 2008 Q2, total employment in New Jersey increased by

111,377 jobs or 2.9 percent, from 3.9 million to 4.0 million (Table 1). This 2.9 percent

growth was considerably below the national 7.2 percent increase for the same time

period.

Employment growth in the state was concentrated in both the private service-

providing sector (+134,173 jobs or +4.9 percent) and government (+34,445 jobs or +5.7

percent). Public-sector employment growth, while smaller in absolute magnitude than

private service-providing employment growth, had a higher rate of increase (5.7 percent

versus 4.8 percent).

In contrast, employment in the private goods-producing sector declined by 57,241

jobs or -10.6 percent. This decline was largely due to the state’s long-term

manufacturing hemorrhage. New Jersey lost 64,377 manufacturing jobs between 2002

and 2008, or -17.6 percent. Thus, nearly one out five of the state’s manufacturing jobs

disappeared in a brief six-year period. In the goods-producing sector, this loss was only

partially counter-balanced by growth in construction employment (+6,315 jobs or +3.9

percent).

Education and health services had the largest employment increase of all of the

service-providing industries. Between 2002 Q2 and 2008 Q2, this sector gained 72,285

jobs, a rate of increase of 15.1 percent, and it accounted for 64.9 percent of the state’s

total employment growth (72,285 jobs out of 111,377 jobs). It was followed by leisure

and hospitality (+41,553 jobs), professional and business services (+40,139 jobs), other

38

services (+16,604 jobs), and finance (+7,028 jobs). Employment losses were suffered by

information (-20,761 jobs), the consequence of the bursting of the telecommunications

bubble, and trade, transportation, and utilities (-2,266 jobs).

Table 1 New Jersey Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change Q2 Q2 Number PercentTOTAL NONFARM 3,896,534 4,007,911 111,377 2.9 TOTAL PRIVATE SECTOR 3,289,608 3,366,541 76,933 2.3 GOODS PRODUCING 538,877 481,636 -57,241 -10.6

Natural Resources And Mining 12,283 13,104 821 6.7Construction 160,620 166,936 6,315 3.9Manufacturing 365,973 301,596 -64,377 -17.6

PRIVATE SERVICE-PROVIDING 2,750,732 2,884,905 134,173 4.9

Trade, Transportation & Utilities 860,135 857,869 -2,266 -0.3Information 113,187 92,426 -20,761 -18.3Financial Activities 255,107 262,136 7,028 2.8Professional And Business Services 577,582 617,721 40,139 6.9Education & Health Services 477,974 550,259 72,285 15.1Leisure And Hospitality 310,673 352,226 41,553 13.4Other Services 113,594 130,198 16,604 14.6

GOVERNMENT 606,925 641,370 34,445 5.7 Source: U.S. Bureau of Labor Statistics.

The broad pattern of employment change for this period can be characterized as

modest below-average overall employment growth and contraction in the goods-

producing sector, led by manufacturing. The largest absolute advances were in the

private service-providing sector, and the highest rate of growth was in the public sector.

Within the private service-providing sector, education and health services dominated,

accounting for nearly two-thirds of the state’s total employment growth. These statewide

patterns are the baseline for the individual county analyses which follow.

39

Essex County

Essex County had the largest economy of the four counties in 2008 as measured

by total employment (363,038 jobs), and detailed in Table 2. During the 2002-2008

period, it experienced growth in private service-providing employment and government

employment, and a decline in goods-producing employment, the same pattern as

exhibited by New Jersey. However, since the losses in the goods-producing sector

exceeded the employment gains in the other two sectors, Essex County experienced an

overall loss of 825 total jobs between 2002 and 2008. It was the only county of the four

to lose employment. Manufacturing had the largest decline with a loss of 6,046 jobs

(-20.4 percent).

Table 2 Essex County Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change Q2 Q2 Number PercentTOTAL NONFARM 363,863 363,038 -825 -0.2 TOTAL PRIVATE SECTOR 290,759 286,500 -4,259 -1.5 GOODS PRODUCING 41,066 34,409 -6,658 -16.2

Natural Resources And Mining 34 43 9 26.2Construction 11,390 10,770 -620 -5.4Manufacturing 29,642 23,596 -6,046 -20.4

PRIVATE SERVICE-PROVIDING 249,692 252,091 2,399 1.0

Trade, Transportation & Utilities 75,327 76,961 1,634 2.2Information 9,046 6,315 -2,731 -30.2Financial Activities 27,727 25,228 -2,498 -9.0Professional And Business Services 50,439 50,508 69 0.1Education & Health Services 53,005 56,352 3,348 6.3Leisure And Hospitality 19,753 23,241 3,488 17.7Other Services 12,600 13,238 638 5.1

GOVERNMENT 73,105 76,539 3,434 4.7 Source: U.S. Bureau of Labor Statistics.

40

Leisure and hospitality had the highest employment gain (+3,488 jobs), followed

closely by education and health services (+3,348 jobs) and government (+3,434 jobs).

The loss of information employment in Essex County (-2,731 jobs) occurred at a rate

greater than that of New Jersey (-30.2 percent versus -18.3 percent). In direct contrast to

the state, Essex County lost employment in financial activities (-2,498 jobs). Also in

contrast to New Jersey, Essex County gained employment in trade, transportation, and

utilities (+1,634 jobs), led by booming port, logistical, and distribution activities. And its

modest gain in professional and business services – just 69 jobs (+0.1 percent) – stood in

marked contrast to strong growth statewide (6.9 percent).

Essex stands as the county employment-growth laggard, with pronounced job

losses in manufacturing, information, and finance. Leisure and hospitality, education and

health services, and government were the dominant growth sectors.

Morris County

Morris County ranked second among the four counties in total employment in

2008 (289,095 jobs), as shown in Table 3. Between 2002 and 2008, employment in

Morris County grew slightly faster than in New Jersey (3.3 percent versus 2.9 percent).

Its growth profile is very similar to that of the state as a whole with one exception –

manufacturing (Table 4). Morris County actually added 894 manufacturing jobs between

2002 and 2008 (+3.6 percent), largely due to the modest expansion of the pharmaceutical

industry, which is mostly categorized under manufacturing. As a result, the goods-

producing sector experienced positive growth (+4.8 percent) in sharp contrast to New

Jersey’s decline (-10.6 percent).

Similar to the state, Morris County had employment declines in two of the seven

service-providing sectors – trade, transportation, and utilities (-4,291 jobs) and

information (-3,846 jobs). Of the five growth sectors, education and health services had

the strongest gains (+5,609 jobs), followed by leisure and hospitality (+4,430 jobs), other

services (+1,479 jobs), professional and business services (+1,531 jobs), and finance (+49

jobs). Government employment (+2,896 jobs) grew at a rate higher than the state (+9.7

percent versus 5.7 percent).

41

Table 3 Morris County Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change Q2 Q2 Number PercentTOTAL NONFARM 279,922 289,095 9,173 3.3 TOTAL PRIVATE SECTOR 250,086 256,363 6,277 2.5 GOODS PRODUCING 36,917 38,704 1,786 4.8

Natural Resources And Mining 476 435 -41 -8.6Construction 11,795 12,729 934 7.9Manufacturing 24,646 25,540 894 3.6

PRIVATE SERVICE-PROVIDING 213,169 217,659 4,490 2.1

Trade, Transportation & Utilities 61,093 56,802 -4,291 -7.0Information 11,990 8,144 -3,846 -32.1Financial Activities 26,564 26,613 49 0.2Professional And Business Services 58,948 60,479 1,531 2.6Education & Health Services 28,943 34,552 5,609 19.4Leisure And Hospitality 16,643 20,984 4,340 26.1Other Services 8,278 9,757 1,479 17.9

GOVERNMENT 29,836 32,732 2,896 9.7 Source: U.S.Bureau of Labor Statistics.

The pattern of growth and decline across Morris County’s employment sectors

largely mirrored that of New Jersey, with the exception of positive manufacturing gains

in the county.

Sussex County

The growth in total employment in Sussex County (+7.3 percent), as shown in

Table 4, was more than double that of New Jersey (+2.9 percent). This rate of increase

was the fastest of any of the four counties. However, because of the county’s relatively

small employment base (40,407 total jobs), the overall increase amounted to only 2,759

jobs. In contrast to the state, the goods-producing sector in Sussex County experienced

growth, despite a significant rate of decline in natural resources and mining employment

42

(-24.7 percent). Manufacturing employment was basically flat, while construction had

strong growth (+198 jobs or +8.3 percent).

Table 4 Sussex County Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change Q2 Q2 Number PercentTOTAL NONFARM 37,648 40,407 2,759 7.3 TOTAL PRIVATE SECTOR 30,394 32,147 1,753 5.8 GOODS PRODUCING 4,519 4,689 170 3.8

Natural Resources And Mining 151 114 -37 -24.7Construction 2,388 2,586 198 8.3Manufacturing 1,980 1,989 9 0.5

PRIVATE SERVICE-PROVIDING 25,875 27,458 1,583 6.1

Trade, Transportation & Utilities 7,512 7,195 -317 -4.2Information 482 476 -6 -1.2Financial Activities 1,186 1,451 265 22.3Professional And Business Services 4,876 4,726 -151 -3.1Education & Health Services 5,796 6,729 933 16.1Leisure And Hospitality 4,346 4,673 326 7.5Other Services 1,333 1,764 431 32.4

GOVERNMENT 7,254 8,260 1,006 13.9 Source: U.S. Bureau of Labor Statistics.

Government (+1,006 jobs) was the biggest individual growth sector. It was

followed by education and health services (+933 jobs), other services (+431 jobs), leisure

and hospitality (+326 jobs), and financial services (+265 jobs). Employment contracted

in trade, transportation, and utilities (-317 jobs), professional and business services (-151

jobs), and information (-6 jobs).

While employment growth was faster than the state, the pattern of Sussex County

employment changes was generally consistent with the state growth template, with two

major differences. First, the county experienced surprising losses in professional and

43

business services employment, and second, its manufacturing sector demonstrated job

stability in contrast to the significant losses experienced statewide.

Warren County

Warren County has the smallest economy, comprising just 37,955 jobs in 2008, as

detailed in Table 5. This total was slightly below that of Sussex County (40,407 jobs).

Its overall employment growth rate (+3.2 percent or +1,186 jobs) between 2002 and 2008

was slightly above that of the state (+2.9 percent). The general statewide pattern of

growth in service-providing and government employment, and decline in goods-

producing employment was also evident in Warren County.

Table 5 Warren County Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change Q2 Q2 Number Percent TOTAL NONFARM 36,770 37,955 1,186 3.2 TOTAL PRIVATE SECTOR 30,881 31,350 469 1.5 GOODS PRODUCING 8,525 8,175 -350 -4.1

Natural Resources And Mining 286 387 101 35.2Construction 1,476 1,953 477 32.3Manufacturing 6,762 5,834 -928 -13.7

PRIVATE SERVICE-PROVIDING 22,356 23,175 819 3.7

Trade, Transportation & Utilities 8,968 8,418 -550 -6.1Information 305 336 31 10.1Financial Activities 954 897 -57 -5.9Professional And Business Services 2,590 2,750 160 6.2Education & Health Services 5,181 6,220 1,039 20.1Leisure And Hospitality 2,976 3,115 138 4.6Other Services 1,125 1,361 236 20.9

GOVERNMENT 5,889 6,606 717 12.2 Source: U.S. Bureau of Labor Statistics.

44

Within the goods-producing sector, which lost 350 jobs, the decline in

manufacturing employment (-928 jobs) overweighed the combined growth in

construction (+477 jobs) and in natural resources and mining (+101 jobs). In the service-

providing sector (+819 jobs), education and health services added 1,039 jobs (+20.1

percent), the highest of any private-sector employment category. Also growing at above-

state average rates were other services (+20.9 percent or +236 jobs), information (+10.1

percent or +31 jobs), professional and business services (+6.2 percent or 160 jobs), and

leisure and hospitality (+4.6 percent or 138 jobs). Employment losses were suffered in

trade, transportation, and utilities (-550 jobs) and financial activities (-57 jobs).

Government employment, grew by 717 jobs (+12.2 percent).

Warren County’s growth pattern during the 2002 and 2008 period was much more

dominated by government compared to New Jersey. Government accounted for 60.4

percent of Warren’s total employment growth (717 jobs out of a total 1,186 jobs). For

New Jersey as a whole, government accounted for 30.9 percent (34,445 jobs out of

111,377 jobs). While there were some minor differences in several sectors, the same

dynamic of goods-producing employment contraction and service-providing employment

expansion was evident in both Warren County and New Jersey.

The Four County Region

Table 6 provides the four county aggregate employment levels and change for the

2002-2008 period, along with the percentage change for the state as a whole.

Employment in he four counties combined is growing somewhat slower than the New

Jersey, mainly due to mature Essex County, which is virtually built out. But outside of

this distinction, aggregating the four counties together tends to mute the individual

county differences. This aggregation results in a region where the broad patterns of

change – or the order of magnitude of the changes – are relatively close to those of the

state as a whole. Thus, the region strongly mirrors New Jersey.

45

Table 6 Four County Aggregate Employment Change by NAICS Supersector

2nd Quarter 2002- 2nd Quarter 2008 2002 2008 Change NJ Q2 Q2 Number Percent PercentTOTAL NONFARM 718,203 730,495 12,292 1.7 2.9 TOTAL PRIVATE SECTOR 602,120 606,359 4,240 0.7 2.3 GOODS PRODUCING 91,028 85,976 -5,052 -5.5 -10.6

Natural Resources And Mining 948 979 31 3.3 6.7Construction 27,049 28,038 989 3.7 3.9Manufacturing 63,031 56,959 -6,072 -9.6 -17.6

PRIVATE SERVICE-PROVIDING 511,092 520,383 9,291 1.8 4.9

Trade, Transportation & Utilities 152,899 149,375 -3,524 -2.3 -0.3Information 21,823 15,270 -6,553 -30.0 -18.3Financial Activities 56,430 54,189 -2,241 -4.0 2.8Professional And Business Services 116,853 118,463 1,610 1.4 6.9Education & Health Services 92,925 103,854 10,929 11.8 15.1Leisure And Hospitality 43,719 52,012 8,293 19.0 13.4Other Services 23,336 26,120 2,784 11.9 14.6

GOVERNMENT 116,084 124,136 8,052 6.9 5.7 Source: U.S. Bureau of Labor Statistics.

Employment Profiles

The 2008 employment profiles of New Jersey and the four counties are presented

in Table 7. The first two numerical columns of Table 7 show the basic profile of the

state: Thus, 72.0 percent of New Jersey’s total employment base was in the private

service-providing sector, 16.0 percent in government, and 12.0 percent in the goods-

producing sector. The last two columns of Table 7 show the equivalent distribution for

the four counties aggregated together: 71.2 percent of the four county region’s total

employment base was in the private service-providing sector, 17 percent in government,

and 11.8 percent in the goods-producing sector. Thus, the broad economic profiles of the

state and region are virtually identical, with the largest difference for the three categories

only a single percentage point. The same is true when the 10 detailed employment

46

Table 7. New Jersey and Selected Counties Employment Shares by Sector

2008 Q2 Employment Data

NJ Essex Morris Sussex Warren Four County Absolute Share Absolute Share Absolute Share Absolute Share Absolute Share Absolute Share TOTAL NONFARM 4,007,911 100.0 363,038 100.0 289,095 100.0 40,407 100.0 37,955 100.0 730,495 100.0 TOTAL PRIVATE SECTOR 3,366,541 84.0 286,500 78.9 256,363 88.7 32,147 79.6 31,350 82.6 606,359 83.0 GOODS PRODUCING 481,636 12.0 34,409 9.5 38,704 13.4 4,689 11.6 8,175 21.5 85,976 11.8

Natural Resources And Mining 13,104 0.3 43 0.0 435 0.2 114 0.3 387 1.0 979 0.1 Construction 166,936 4.2 10,770 3.0 12,729 4.4 2,586 6.4 1,953 5.1 28,038 3.8 Manufacturing 301,596 7.5 23,596 6.5 25,540 8.8 1,989 4.9 5,834 15.4 56,959 7.8

PRIVATE SERVICE-PROVIDING 2,884,905 72.0 252,091 69.4 217,659 75.3 27,458 68.0 23,175 61.1 520,383 71.2

Trade, Transportation & Utilities 857,869 21.4 76,961 21.2 56,802 19.6 7,195 17.8 8,418 22.2 149,375 20.4 Information 92,426 2.3 6,315 1.7 8,144 2.8 476 1.2 336 0.9 15,270 2.1 Financial Activities 262,136 6.5 25,228 6.9 26,613 9.2 1,451 3.6 897 2.4 54,189 7.4 Professional And Business Services 617,721 15.4 50,508 13.9 60,479 20.9 4,726 11.7 2,750 7.2 118,463 16.2 Education & Health Services 550,259 13.7 56,352 15.5 34,552 12.0 6,729 16.7 6,220 16.4 103,854 14.2 Leisure And Hospitality 352,226 8.8 23,241 6.4 20,984 7.3 4,673 11.6 3,115 8.2 52,012 7.1 Other Services 130,198 3.2 13,238 3.6 9,757 3.4 1,764 4.4 1,361 3.6 26,120 3.6

GOVERNMENT 641,370 16.0 76,539 21.1 32,732 11.3 8,260 20.4 6,606 17.4 124,136 17.0 Source: U.S. Bureau of Labor Statistics.

47

sectors are compared. In only two sectors – trade, transportation, and utilities (1.0

percentage points) and leisure and hospitality (1.7 percentage points) – is the difference

in share equal to or greater than a single percentage point. The difference in share for the

other eight detailed sectors is less than a single percentage point.

There is much more variation when the individual county profiles are compared to

the state and with one another. Morris County has the highest share of its total

employment in the service-providing sector (75.3 percent) while Warren County had the

lowest share (61.1 percent). In contrast, Warren County had the highest share of its total

employment in the goods-producing sector (21.5 percent), while Essex had the lowest

(9.5 percent). The government sector was most pronounced in Essex County, where it

accounted for 21.1 percent of total employment. In contrast, government accounted for

only 11.3 percent of total employment in Morris County.

Of the major individual sectors, construction employment had the greatest

proportional representation in Sussex County (6.4 percent of total employment) while

Essex County had the least (3.0 percent). Manufacturing employment had its greatest

proportional share in Warren County (15.4 percent) and its lowest in Sussex County (4.9

percent). Trade, transportation, and utilities employment had a much smaller variation

among the counties, with its share ranging from a low of 17.8 percent (Sussex County)

and a high of 22.2 percent (Warren County).

Information employment had its highest share in Morris County (2.8 percent) and

its lowest share in Warren County (0.9 percent). The same pattern prevailed in financial

activities and professional and business services employment. Morris County had the

greatest share of its total employment in financial activities (9.2 percent) and Warren

County had the lowest (2.4 percent). Similarly, Morris County had the highest share of

its total employment in professional and business services (20.9 percent) while Warren

County had the lowest (7.2 percent).

Education and health services employment had only minor variations in the four

counties relative to its 13.7 percent share of the state’s total employment, with the highest

share in Sussex County (16.7 percent) and the lowest share in Morris County (12.0

percent). The outliers in leisure and hospitality employment were Sussex County, where

this sector had the highest share (11.6 percent), and Essex County, where it had the

48

lowest share (6.4 percent). Other services’ share had small variations between 3.4

percent and 4.4 percent.

These distributional patterns reveal that the economies of the four individual

counties do reflect the economic structure of the state as a whole, but at the same time

they each have some unique features and specializations. Most importantly, Morris and

Essex counties each have large powerful office markets that compete with nation’s

largest metropolitan areas. Morris County ranked first among the 21 counties in the state

with more than 30.3 million square feet of office space, while Essex County ranks fourth

with 28.8 million square feet of office space. Thus, these two large counties have the

greatest concentrations of high-end jobs in information, finance, and professional and

business services. Sussex and Warren counties have very much smaller office

inventories, particularly Class A office buildings (the most attractive, technologically-

equipped, investment-grade properties). Information, finance and professional and

business services jobs in these two counties tend to be more population oriented, i.e.,

providing services to households and individuals, rather than serving much broader state

and national markets, as is the case in Morris and Essex counties.

Demographics

The four-county region in total accounted for 17.5 percent (1,519,008 persons out

of 8,682,661 persons) of New Jersey’s population in 2008, a slightly lower percentage

than its share of the state’s total payroll employment (18.2 percent). Essex County

dominated in terms of absolute size (770,675 persons), followed by Morris County

(487,548 persons), as shown in Table 8. Sussex County’s population totaled only

150,909 persons in 2008, while Warren County’s population was even smaller (109,875

persons). As was the case with employment, Essex County has the highest population

density, as it contains New Jersey’s largest city as well as some of its densest older

suburbs. Morris County is far more suburban in character, while Sussex County is

mostly low-density rural and secondarily suburban. Warren County is a mixture of

suburban and rural, with a small urbanized area surrounding Phillipsburg. So the

residential development and population density variations within the four-county region

tend mirror the wide variations evident in the state as a whole.

49

2001 2002 2003 2004 2005 2006 2007 2008NJ 8,490,942 8,547,410 8,589,562 8,620,770 8,634,657 8,640,218 8,653,126 8,682,661Essex 793,238 793,280 791,203 786,346 780,189 775,041 772,273 770,675Morris 473,281 476,692 479,944 482,762 484,003 485,658 486,172 487,548Sussex 146,046 147,891 149,506 150,318 150,729 151,030 151,257 150,909Warren 105,270 106,871 108,232 108,607 109,000 109,265 109,492 109,876

2000-2008 % Resid. TOTAL %

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 *NJ 60,029 56,468 42,152 31,208 13,887 5,561 12,908 29,535 251,748 3.0 21,652 273,400 3.2Essex 919 42 -2,077 -4,857 -6,157 -5,148 -2,768 -1,598 -21,644 -2.7 4,485 -17,159 -2.2Morris 1,933 3,411 3,252 2,818 1,241 1,655 514 1,376 16,200 3.4 2,613 18,813 4.0Sussex 1,436 1,845 1,615 812 411 301 227 -348 6,299 4.3 693 6,992 4.8Warren 2,323 1,601 1,361 375 393 265 227 384 6,929 6.6 390 7,319 7.0

2000-2008

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008NJ 39,386 38,025 42,594 42,804 39,857 39,663 44,876 46,272 333,477Essex 4,781 4,838 5,371 5,351 5,061 5,099 5,853 5,659 42,013Morris 2,936 2,800 2,979 2,861 2,494 2,313 2,233 2,165 20,781Sussex 792 690 701 643 537 634 630 660 5,287Warren 521 430 521 318 402 370 403 456 3,421

2000-2008

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008NJ 23,665 21,165 3,071 -8,422 -22,947 -31,434 -30,763 -14,412 -60,077Essex -3,158 -4,190 -6,989 -9,327 -10,695 -9,904 -8,292 -6,617 -59,172Morris -623 1,033 888 322 -823 -430 -1,634 -701 -1,968Sussex 775 1,280 1,076 261 9 -265 -411 -1,020 1,705Warren 1,862 1,249 943 128 89 -53 -192 -128 3,898

2000-2008

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008NJ 55,813 52,214 45,346 42,799 44,393 46,205 41,607 41,796 370,173Essex 6,442 6,089 5,413 4,879 5,158 5,393 4,895 4,898 43,167Morris 2,855 2,671 2,355 2,135 2,252 2,284 2,077 2,081 18,710Sussex 157 149 128 132 135 141 116 117 1,075Warren 213 205 180 170 176 188 163 164 1,459

2000-2008

2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008NJ -32,148 -31,049 -42,275 -51,221 -67,340 -77,639 -72,370 -56,208 -430,250Essex -9,600 -10,279 -12,402 -14,206 -15,853 -15,297 -13,187 -11,515 -102,339Morris -3,478 -1,638 -1,467 -1,813 -3,075 -2,714 -3,711 -2,782 -20,678Sussex 618 1,131 948 129 -126 -406 -527 -1,137 630Warren 1,649 1,044 763 -42 -87 -241 -355 -292 2,439

Source: US Census Population Estimates.

Table 8.New Jersey and selected county population change and components, 2000-2008

Population

International

Net Migration

** These data may also differ from previous state population data, which have since been revised.

Change

Natural

Domestic

*The cumulative data are underestimated by the residual amount. The total is the change (cumulative + residual) from July 2000 - July 2008. These data may differ from other reports that begin from April 2000.

50

Table 8 also provides year-by year population levels for New Jersey and the four

counties, as well as the annual changes in population. Population change has three major

components: net natural increase (births minus deaths), net international immigration (the

difference between the number of people from outside the United States moving into

New Jersey and the number of people from New Jersey moving outside the country), and

net domestic migration (the difference between the number of people from New Jersey

moving to the rest of the United States and the number of people from the rest of the

country moving into New Jersey). Net migration is the sum of international and domestic

migration. All of these components are detailed in Table 8 for each county and the state.

A technical note is warranted here. When the Census Bureau tabulates this data,

the sum of the components (which yields the net annual population change) this

sometimes differs slightly from the net annual change as computed directly from annual

population totals. This difference is what the Census Bureau calls the residual. So the

final change data for the 2000-2008 period, presented in the far right column of Table 8,

includes the residual. Between 2000 and 2008, New Jersey’s population grew by

273,400 people (+3.2 percent), a pace far slower than that of the nation (+7.8 percent).

The four-county region had a 2000-2008 population growth of 15,965 persons (+1.1

percent). So, the four-county region was growing slower than New Jersey and far slower

than the United States (7.8 percent).

This is largely due to the absolute population decline that took place in Essex

County during this period (-17,159 persons or -2.2 percent). Positive growth was

registered by the other three counties, led by Warren County (+7,319 persons or +7.0

percent). It is important to note that even Warren County’s growth rate lagged that of the

nation. Sussex County had the second highest growth rate (+4.8 percent or +6,992

persons) followed closely by Morris County (+4.0 percent or 18,813 persons).

Much of this growth took place in the early years of the 2000-2008 period. For

example, New Jersey’s overall population increase totaled 60,029 persons in 2000-2001.

The annual net growth increment then steadily declined to just 5,561 persons in 2005-

2006, before rebounding in the next two years. The slowdown in growth was largely due

to a surge of domestic migration losses, from -32,148 persons in 2000-2001 to -77,639

51

persons in 2005-2006. So the wave of New Jerseyans moving to other states reduced the

state’s overall annual population growth almost down to zero by 2005-2006.

However, there was a growth rebound in the last two years of this period. This

rebound was due an increase in the natural component of population (births minus

deaths) which rose from 39,663 in 2005-2006 to 46,272 in 2007-2008. It was also caused

by a reduction in domestic mobility caused by the housing bust – if you can’t sell your

house, you can’t move. While New Jersey still had significant domestic outmigration

losses – -72,370 persons in 2006-2007 and -56,208 persons in 2007-2008 – this decline in

net domestic outmigration and the gain in the natural component of population growth

resulted in an increase in the state’s annual population growth to 29,535 persons in 2007-

2008.

As seen in Table 8, net domestic outmigration was initially restricted to Essex and

Morris counties through 2002-2003. Then in 2003-2004, they were joined by Warren

County. By 2004-2005, all four counties were experiencing net domestic migration

losses. And this remained the case for the years that followed: net domestic migration

losses were pervasive in 2005-2006, 2006-2007, and 2007-2008.

For the entire 2000-2008 period, net domestic out migration in New Jersey

(-430,250 persons) was greater than net international immigration (+370,000 persons),

which yielded an overall net migration loss (-60,077 persons). Thus all of the 2000-2008

total population growth in the state (+273,400 persons) was due to net natural increase

(+333,477 persons). This general pattern is reflected in the four-county region, where the

net domestic outmigration (-119,948 persons) was proportionally far greater than net

international immigration (+64,411). As was the case for the state as a whole, population

growth was solely due to net natural increase (+71,502 persons) since international

immigration did not fully counterbalance net domestic migration losses. Since, overall

net migration losses totaled 55,537 persons, the four-county population change totaled

15,965 persons.

To summarize, the four-counties combined had positive population growth

between 2000 and 2003. Starting in 2003-2004, the net annual population change turned

negative. This was largely due to population losses in Essex County. It was also caused

by sharply lower annual gains experienced by the other three counties. The four counties

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combined now basically comprise a very slow-growth demographic region, with all four

individual counties currently experiencing net domestic migration losses. With the full

impact of the Highlands Act yet to be fully realized in terms of developmental growth

restrictions, the four counties are likely to be destined to remain on a slow-growth

population trajectory.

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APPENDIX B: INPUT-OUTPUT ANALYSIS

This appendix discusses the history and application of input-output analysis and

details the input-output model, called the R/Econ™ I-O model, developed by Rutgers

University. This model offers significant advantages in detailing the total economic

effects of an activity (such as historic rehabilitation and heritage tourism), including

multiplier effects.

ESTIMATING MULTIPLIERS

The fundamental issue determining the size of the multiplier effect is the

“openness” of regional economies. Regions that are more “open” are those that import

their required inputs from other regions. Imports can be thought of as substitutes for local

production. Thus, the more a region depends on imported goods and services instead of

its own production, the more economic activity leaks away from the local economy.

Businessmen noted this phenomenon and formed local chambers of commerce with the

explicit goal of stopping such leakage by instituting a “buy local” policy among their

membership. In addition, during the 1970s, as an import invasion was under way,

businessmen and union leaders announced a “buy American” policy in the hope of

regaining ground lost to international economic competition. Therefore, one of the main

goals of regional economic multiplier research has been to discover better ways to

estimate the leakage of purchases out of a region or, relatedly, to determine the region’s

level of self-sufficiency.

The earliest attempts to systematize the procedure for estimating multiplier effects

used the economic base model, still in use in many econometric models today. This

approach assumes that all economic activities in a region can be divided into two

categories: “basic” activities that produce exclusively for export, and region-serving or

“local” activities that produce strictly for internal regional consumption. Since this

approach is simpler but similar to the approach used by regional input-output analysis, let

us explain briefly how multiplier effects are estimated using the economic base approach.

If we let x be export employment, l be local employment, and t be total employment, then

54

t = x + l

For simplification, we create the ratio a as

a = l/t

so that l = at

then substituting into the first equation, we obtain

t = x + at

By bringing all of the terms with t to one side of the equation, we get

t - at = x or t (1-a) = x

Solving for t, we get t = x/(1-a)

Thus, if we know the amount of export-oriented employment, x, and the ratio of

local to total employment, a, we can readily calculate total employment by applying the

economic base multiplier, 1/(1-a), which is embedded in the above formula. Thus, if 40

percent of all regional employment is used to produce exports, the regional multiplier

would be 2.5. The assumption behind this multiplier is that all remaining regional

employment is required to support the export employment. Thus, the 2.5 can be

decomposed into two parts the direct effect of the exports, which is always 1.0, and the

indirect and induced effects, which is the remainder—in this case 1.5. Hence, the

multiplier can be read as telling us that for each export-oriented job another 1.5 jobs are

needed to support it.

This notion of the multiplier has been extended so that x is understood to

represent an economic change demanded by an organization or institution outside of an

economy—so-called final demand. Such changes can be those effected by government,

households, or even by an outside firm. Changes in the economy can therefore be

calculated by a minor alteration in the multiplier formula:

Δt = Δx/(1-a)

The high level of industry aggregation and the rigidity of the economic

assumptions that permit the application of the economic base multiplier have caused this

approach to be subject to extensive criticism. Most of the discussion has focused on the

55

estimation of the parameter a. Estimating this parameter requires that one be able to

distinguish those parts of the economy that produce for local consumption from those that

do not. Indeed, virtually all industries, even services, sell to customers both inside and

outside the region. As a result, regional economists devised an approach by which to

measure the degree to which each industry is involved in the nonbase activities of the

region, better known as the industry’s regional purchase coefficient. Thus, they expanded

the above formulations by calculating for each i industry

li = r idi

and xi = ti - r idi

given that di is the total regional demand for industry i’s product. Given the above

formulae and data on regional demands by industry, one can calculate an accurate

traditional aggregate economic base parameter by the following:

a = l/t = Σlii/Σti

Although accurate, this approach only facilitates the calculation of an aggregate

multiplier for the entire region. That is, we cannot determine from this approach what the

effects are on the various sectors of an economy. This is despite the fact that one must

painstakingly calculate the regional demand as well as the degree to which they each

industry is involved in nonbase activity in the region.

As a result, a different approach to multiplier estimation that takes advantage of

the detailed demand and trade data was developed. This approach is called input-output

analysis.

REGIONAL INPUT-OUTPUT ANALYSIS: A BRIEF HISTORY

The basic framework for input-output analysis originated nearly 250 years ago

when François Quesenay published Tableau Economique in 1758. Quesenay’s “tableau”

graphically and numerically portrayed the relationships between sales and purchases of

the various industries of an economy. More than a century later, his description was

56

adapted by Leon Walras, who advanced input-output modeling by providing a concise

theoretical formulation of an economic system (including consumer purchases and the

economic representation of “technology”).

It was not until the twentieth century, however, that economists advanced and

tested Walras’s work. Wassily Leontief greatly simplified Walras’s theoretical formu-

lation by applying the Nobel prize–winning assumptions that both technology and trading

patterns were fixed over time. These two assumptions meant that the pattern of flows

among industries in an area could be considered stable. These assumptions permitted

Walras’s formulation to use data from a single time period, which generated a great

reduction in data requirements.

Although Leontief won the Nobel Prize in 1973, he first used his approach in

1936 when he developed a model of the 1919 and 1929 U.S. economies to estimate the

effects of the end of World War I on national employment. Recognition of his work in

terms of its wider acceptance and use meant development of a standardized procedure for

compiling the requisite data (today’s national economic census of industries) and

enhanced capability for calculations (i.e., the computer).

The federal government immediately recognized the importance of Leontief’s

development and has been publishing input-output tables of the U.S. economy since

1939. The most recently published tables are those for 1987. Other nations followed suit.

Indeed, the United Nations maintains a bank of tables from most member nations with a

uniform accounting scheme.

Framework

Input-output modeling focuses on the interrelationships of sales and purchases

among sectors of the economy. Input-output is best understood through its most basic

form, the interindustry transactions table or matrix. In this table (see figure 1 for an

example), the column industries are consuming sectors (or markets) and the row

57

industries are producing sectors. The content of a matrix cell is the value of shipments

that the row industry delivers to the column industry. Conversely, it is the value of

shipments that the column industry receives from the row industry. Hence, the

interindustry transactions table is a detailed accounting of the disposition of the value of

shipments in an economy. Indeed, the detailed accounting of the interindustry

transactions at the national level is performed not so much to facilitate calculation of

national economic impacts as it is to back out an estimate of the nation’s gross domestic

product.

FIGURE 1 Interindustry Transactions Matrix (Values)

Agriculture

Manufacturing

Services

Other Final

Demand Total

Output Agriculture 10 65 10 5 10 $100 Manufacturing 40 25 35 75 25 $200 Services 15 5 5 5 90 $120 Other 15 10 50 50 100 $225 Value Added 20 95 20 90 Total Input 100 200 120 225

For example, in figure 1, agriculture, as a producing industry sector, is depicted as

selling $65 million of goods to manufacturing. Conversely, the table depicts that the

manufacturing industry purchased $65 million of agricultural production. The sum across

columns of the interindustry transaction matrix is called the intermediate outputs vector.

The sum across rows is called the intermediate inputs vector.

A single final demand column is also included in Figure 1. Final demand, which

is outside the square interindustry matrix, includes imports, exports, government

purchases, changes in inventory, private investment, and sometimes household purchases.

The value added row, which is also outside the square interindustry matrix, includes

wages and salaries, profit-type income, interest, dividends, rents, royalties, capital

consumption allowances, and taxes. It is called value added because it is the difference

between the total value of the industry’s production and the value of the goods and

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nonlabor services that it requires to produce. Thus, it is the value that an industry adds to

the goods and services it uses as inputs in order to produce output.

The value added row measures each industry’s contribution to wealth

accumulation. In a national model, therefore, its sum is better known as the gross

domestic product (GDP). At the state level, this is known as the gross state product—a

series produced by the U.S. Bureau of Economic Analysis and published in the Regional

Economic Information System. Below the state level, it is known simply as the regional

equivalent of the GDP—the gross regional product.

Input-output economic impact modelers now tend to include the household

industry within the square interindustry matrix. In this case, the “consuming industry” is

the household itself. Its spending is extracted from the final demand column and is

appended as a separate column in the interindustry matrix. To maintain a balance, the

income of households must be appended as a row. The main income of households is

labor income, which is extracted from the value-added row. Modelers tend not to include

other sources of household income in the household industry’s row. This is not because

such income is not attributed to households but rather because much of this other income

derives from sources outside of the economy that is being modeled.

The next step in producing input-output multipliers is to calculate the direct

requirements matrix, which is also called the technology matrix. The calculations are

based entirely on data from figure 1. As shown in figure 2, the values of the cells in the

direct requirements matrix are derived by dividing each cell in a column of figure 1, the

interindustry transactions matrix, by its column total. For example, the cell for

manufacturing’s purchases from agriculture is 65/200 = .33. Each cell in a column of the

direct requirements matrix shows how many cents of each producing industry’s goods

and/or services are required to produce one dollar of the consuming industry’s production

and are called technical coefficients. The use of the terms “technology” and “technical”

derive from the fact that a column of this matrix represents a recipe for a unit of an

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industry’s production. It, therefore, shows the needs of each industry’s production

process or “technology.”

FIGURE 2 Direct Requirements Matrix

Agriculture Manufacturing Services Other

Agriculture .10 .33 .08 .02 Manufacturing .40 .13 .29 .33 Services .15 .03 .04 .02 Other .15 .05 .42 .22

Next in the process of producing input-output multipliers, the Leontief Inverse is

calculated. To explain what the Leontief Inverse is, let us temporarily turn to equations.

Now, from figure 1 we know that the sum across both the rows of the square

interindustry transactions matrix (Z) and the final demand vector (y) is equal to vector of

production by industry (x). That is,

x = Zi + y

where i is a summation vector of ones. Now, we calculate the direct requirements matrix

(A) by dividing the interindustry transactions matrix by the production vector or

A = ZX-1

where X-1 is a square matrix with inverse of each element in the vector x on the diagonal

and the rest of the elements equal to zero. Rearranging the above equation yields

Z = AX

where X is a square matrix with the elements of the vector x on the diagonal and zeros

elsewhere. Thus,

x = (AX)i + y

or, alternatively,

x = Ax + y

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solving this equation for x yields

x = (I-A)-1 y

Total = Total * Final

Output Requirements Demand

The Leontief Inverse is the matrix (I-A)-1. It portrays the relationships between

final demand and production. This set of relationships is exactly what is needed to

identify the economic impacts of an event external to an economy.

Because it does translate the direct economic effects of an event into the total

economic effects on the modeled economy, the Leontief Inverse is also called the total

requirements matrix. The total requirements matrix resulting from the direct requirements

matrix in the example is shown in figure 3.

FIGURE 3 Total Requirements Matrix

Agriculture Manufacturing Services Other

Agriculture 1.5 .6 .4 .3 Manufacturing 1.0 1.6 .9 .7 Services .3 .1 1.2 .1 Other .5 .3 .8 1.4 Industry Multipliers .33 2.6 3.3 2.5

In the direct or technical requirements matrix in Figure 2, the technical coefficient

for the manufacturing sector’s purchase from the agricultural sector was .33, indicating

the 33 cents of agricultural products must be directly purchased to produce a dollar’s

worth of manufacturing products. The same “cell” in Figure 3 has a value of .6. This

indicates that for every dollar’s worth of product that manufacturing ships out of the

economy (i.e., to the government or for export), agriculture will end up increasing its

production by 60 cents. The sum of each column in the total requirements matrix is the

output multiplier for that industry.

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Multipliers

A multiplier is defined as the system of economic transactions that follow a

disturbance in an economy. Any economic disturbance affects an economy in the same

way as does a drop of water in a still pond. It creates a large primary “ripple” by causing

a direct change in the purchasing patterns of affected firms and institutions. The suppliers

of the affected firms and institutions must change their purchasing patterns to meet the

demands placed upon them by the firms originally affected by the economic disturbance,

thereby creating a smaller secondary “ripple.” In turn, those who meet the needs of the

suppliers must change their purchasing patterns to meet the demands placed upon them

by the suppliers of the original firms, and so on; thus, a number of subsequent “ripples”

are created in the economy.

The multiplier effect has three components—direct, indirect, and induced effects.

Because of the pond analogy, it is also sometimes referred to as the ripple effect.

• A direct effect (the initial drop causing the ripple effects) is the change in purchases

due to a change in economic activity.

• An indirect effect is the change in the purchases of suppliers to those economic

activities directly experiencing change.

• An induced effect is the change in consumer spending that is generated by changes in

labor income within the region as a result of the direct and indirect effects of the

economic activity. Including households as a column and row in the interindustry

matrix allows this effect to be captured.

Extending the Leontief Inverse to pertain not only to relationships between total

production and final demand of the economy but also to changes in each permits its

multipliers to be applied to many types of economic impacts. Indeed, in impact analysis

the Leontief Inverse lends itself to the drop-in-a-pond analogy discussed earlier. This is

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because the Leontief Inverse multiplied by a change in final demand can be estimated by

a power series. That is,

(I-A)-1 Δy = Δy + A Δy + A(A Δy) + A(A(A Δy)) + A(A(A(A Δy))) + ...

Assuming that Δy—the change in final demand—is the “drop in the pond,” then

succeeding terms are the ripples. Each “ripple” term is calculated as the previous “pond

disturbance” multiplied by the direct requirements matrix. Thus, since each element in

the direct requirements matrix is less than one, each ripple term is smaller than its

predecessor. Indeed, it has been shown that after calculating about seven of these ripple

terms that the power series approximation of impacts very closely estimates those

produced by the Leontief Inverse directly.

In impacts analysis practice, Δy is a single column of expenditures with the same

number of elements as there are rows or columns in the direct or technical requirements

matrix. This set of elements is called an impact vector. This term is used because it is the

vector of numbers that is used to estimate the economic impacts of the investment.

There are two types of changes in investments, and consequently economic

impacts, generally associated with projects—one-time impacts and recurring impacts.

One-time impacts are impacts that are attributable to an expenditure that occurs once over

a limited period of time. For example, the impacts resulting from the construction of a

project are one-time impacts. Recurring impacts are impacts that continue permanently as

a result of new or expanded ongoing expenditures. The ongoing operation of a new train

station, for example, generates recurring impacts to the economy. Examples of changes in

economic activity are investments in the preservation of old homes, tourist expenditures,

or the expenditures required to run a historical site. Such activities are considered

changes in final demand and can be either positive or negative. When the activity is not

made in an industry, it is generally not well represented by the input-output model.

Nonetheless, the activity can be represented by a special set of elements that are similar

to a column of the transactions matrix. This set of elements is called an economic

disturbance or impact vector. The latter term is used because it is the vector of numbers

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that is used to estimate the impacts. In this study, the impact vector is estimated by

multiplying one or more economic translators by a dollar figure that represents an

investment in one or more projects. The term translator is derived from the fact that such

a vector translates a dollar amount of an activity into its constituent purchases by

industry.

One example of an industry multiplier is shown in figure 4. In this example, the

activity is the preservation of a historic home. The direct impact component consists of

purchases made specifically for the construction project from the producing industries.

The indirect impact component consists of expenditures made by producing industries to

support the purchases made for this project. Finally, the induced impact component

focuses on the expenditures made by workers involved in the activity on-site and in the

supplying industries.

FIGURE 4 Components of the Multiplier for the

Historic Rehabilitation of a Single-Family Residence

DIRECT IMPACT INDIRECT IMPACT INDUCED IMPACT Excavation/Construction Labor Concrete Wood Bricks Equipment Finance and Insurance

Production Labor Steel Fabrication Concrete Mixing Factory and Office Expenses Equipment Components

Expenditures by wage earners on-site and in the supplying industries for food, clothing, durable goods, entertainment

REGIONAL INPUT-OUTPUT ANALYSIS

Because of data limitations, regional input-output analysis has some

considerations beyond those for the nation. The main considerations concern the

depiction of regional technology and the adjustment of the technology to account for

interregional trade by industry.

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In the regional setting, local technology matrices are not readily available. An

accurate region-specific technology matrix requires a survey of a representative sample

of organizations for each industry to be depicted in the model. Such surveys are

extremely expensive.4 Because of the expense, regional analysts have tended to use

national technology as a surrogate for regional technology. This substitution does not

affect the accuracy of the model as long as local industry technology does not vary

widely from the nation’s average.5

Even when local technology varies widely from the nation’s average for one or

more industries, model accuracy may not be affected much. This is because interregional

trade may mitigate the error that would be induced by the technology. That is, in

estimating economic impacts via a regional input-output model, national technology must

be regionalized by a vector of regional purchase coefficients,6 r, in the following manner:

(I-rA)-1 r⋅Δy

or

r⋅Δy + rA (r⋅Δy) + rA(rA (r⋅Δy)) + rA(rA(rA (r⋅Δy))) + ...

where the vector-matrix product rA is an estimate of the region’s direct requirements

matrix. Thus, if national technology coefficients—which vary widely from their local

equivalents—are multiplied by small RPCs, the error transferred to the direct

requirements matrices will be relatively small. Indeed, since most manufacturing

industries have small RPCs and since technology differences tend to arise due to

substitution in the use of manufactured goods, technology differences have generally

been found to be minor source error in economic impact measurement. Instead, RPCs and

4The most recent statewide survey-based model was developed for the State of Kansas in 1986 and cost on the order of $60,000 (in 1990 dollars). The development of this model, however, leaned heavily on work done in 1965 for the same state. In addition the model was aggregated to the 35-sector level, making it inappropriate for many possible applications since the industries in the model do not represent the very detailed sectors that are generally analyzed. 5Only recently have researchers studied the validity of this assumption. They have found that large urban areas may have technology in some manufacturing industries that differs in a statistically significant way from the national average. As will be discussed in a subsequent paragraph, such differences may be unimportant after accounting for trade patterns. 6A regional purchase coefficient (RPC) for an industry is the proportion of the region’s demand for a good or service that is fulfilled by local production. Thus, each industry’s RPC varies between zero (0) and one (1), with one implying that all local demand is fulfilled by local suppliers. As a general rule, agriculture, mining, and manufacturing industries tend to have low RPCs, and both service and construction industries tend to have high RPCs.

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their measurement error due to industry aggregation have been the focus of research on

regional input-output model accuracy.

A COMPARISON OF THREE MAJOR REGIONAL ECONOMIC IMPACT

MODELS

In the United States there are three major vendors of regional input-output

models. They are U.S. Bureau of Economic Analysis’s (BEA) RIMS II multipliers,

Minnesota IMPLAN Group Inc.’s (MIG) IMPLAN Pro model, and CUPR’s own

REcon™ I–O model. CUPR has had the privilege of using them all. (R/Econ™ I–O

builds from the PC I–O model produced by the Regional Science Research Corporation’s

(RSRC).)

Although the three systems have important similarities, there are also significant

differences that should be considered before deciding which system to use in a particular

study. This document compares the features of the three systems. Further discussion can

be found in Brucker, Hastings, and Latham’s article in the Summer 1987 issue of The

Review of Regional Studies entitled “Regional Input-Output Analysis: A Comparison of

Five Ready-Made Model Systems.” Since that date, CUPR and MIG have added a

significant number of new features to PC I–O (now, R/Econ™ I–O) and IMPLAN,

respectively.

Model Accuracy

RIMS II, IMPLAN, and RECON™ I–O all employ input-output (I–O) models for

estimating impacts. All three regionalized the U.S. national I–O technology coefficients

table at the highest levels of disaggregation (more than 500 industries). Since aggregation

of sectors has been shown to be an important source of error in the calculation of impact

multipliers, the retention of maximum industrial detail in these regional systems is a

positive feature that they share. The systems diverge in their regionalization approaches,

however. The difference is in the manner that they estimate regional purchase

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coefficients (RPCs), which are used to regionalize the technology matrix. An RPC is the

proportion of the region’s demand for a good or service that is fulfilled by the region’s

own producers rather than by imports from producers in other areas. Thus, it expresses

the proportion of the purchases of the good or service that do not leak out of the region,

but rather feed back to its economy, with corresponding multiplier effects. Thus, the

accuracy of the RPC is crucial to the accuracy of a regional I–O model, since the regional

multiplier effects of a sector vary directly with its RPC.

The techniques for estimating the RPCs used by CUPR and MIG in their models

are theoretically more appealing than the location quotient (LQ) approach used in RIMS

II. This is because the former two allow for crosshauling of a good or service among

regions and the latter does not. Since crosshauling of the same general class of goods or

services among regions is quite common, the CUPR-MIG approach should provide better

estimates of regional imports and exports. Statistical results reported in Stevens, Treyz,

and Lahr (1989) confirm that LQ methods tend to overestimate RPCs. By extension,

inaccurate RPCs may lead to inaccurately estimated impact estimates.

Further, the estimating equation used by CUPR to produce RPCs should be more

accurate than that used by MIG. The difference between the two approaches is that MIG

estimates RPCs at a more aggregated level (two-digit SICs, or about 86 industries) and

applies them at a desegregate level (over 500 industries). CUPR both estimates and

applies the RPCs at the most detailed industry level. The application of aggregate RPCs

can induce as much as 50 percent error in impact estimates (Lahr and Stevens, 2002).

Although both RECON™ I–O and IMPLAN use an RPC-estimating technique

that is theoretically sound and update it using the most recent economic data, some

practitioners question their accuracy. The reasons for doing so are three-fold. First, the

observations currently used to estimate their implemented RPCs are based on 20-years

old trade relationships—the Commodity Transportation Survey (CTS) from the 1977

Census of Transportation. Second, the CTS observations are at the state level. Therefore,

RPC’s estimated for substate areas are extrapolated. Hence, there is the potential that

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RPCs for counties and metropolitan areas are not as accurate as might be expected. Third,

the observed CTS RPCs are only for shipments of goods. The interstate provision of

services is unmeasured by the CTS. IMPLAN replies on relationships from the 1977 U.S.

Multiregional Input-Output Model that are not clearly documented. RECON™ I–O relies

on the same econometric relationships that it does for manufacturing industries but

employs expert judgment to construct weight/value ratios (a critical variable in the RPC-

estimating equation) for the nonmanufacturing industries.

The fact that BEA creates the RIMS II multipliers gives it the advantage of being

constructed from the full set of the most recent regional earnings data available. BEA is

the main federal government purveyor of employment and earnings data by detailed

industry. It therefore has access to the fully disclosed and disaggregated versions of these

data. The other two model systems rely on older data from County Business Patterns and

Bureau of Labor Statistic’s ES202 forms, which have been “improved” by filling-in for

any industries that have disclosure problems (this occurs when three or fewer firms exist

in an industry or a region).

Model Flexibility

For the typical user, the most apparent differences among the three modeling

systems are the level of flexibility they enable and the type of results that they yield.

R/Econ™ I–O allows the user to make changes in individual cells of the 515-by-515

technology matrix as well as in the 11 515-sector vectors of region-specific data that are

used to produce the regionalized model. The 11 sectors are: output, demand, employment

per unit output, labor income per unit output, total value added per unit of output, taxes

per unit of output (state and local), nontax value added per unit output, administrative and

auxiliary output per unit output, household consumption per unit of labor income, and the

RPCs. Te PC I–O model tends to be simple to use. Its User’s Guide is straightforward

and concise, providing instruction about the proper implementation of the model as well

as the interpretation of the model’s results.

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The software for IMPLAN Pro is Windows-based, and its User’s Guide is more

formalized. Of the three modeling systems, it is the most user-friendly. The Windows

orientation has enabled MIG to provide many more options in IMPLAN without

increasing the complexity of use. Like R/Econ™ I–O, IMPLAN’s regional data on RPCs,

output, labor compensation, industry average margins, and employment can be revised. It

does not have complete information on tax revenues other than those from indirect

business taxes (excise and sales taxes), and those cannot be altered. Also like R/Econ™,

IMPLAN allows users to modify the cells of the 538-by-538 technology matrix. It also

permits the user to change and apply price deflators so that dollar figures can be updated

from the default year, which may be as many as four years prior to the current year. The

plethora of options, which are advantageous to the advanced user, can be extremely

confusing to the novice. Although default values are provided for most of the options, the

accompanying documentation does not clearly point out which items should get the most

attention. Further, the calculations needed to make any requisite changes can be more

complex than those needed for the R/Econ™ I–O model. Much of the documentation for

the model dwells on technical issues regarding the guts of the model. For example, while

one can aggregate the 538-sector impacts to the one- and two-digit SIC level, the current

documentation does not discuss that possibility. Instead, the user is advised by the Users

Guide to produce an aggregate model to achieve this end. Such a model, as was discussed

earlier, is likely to be error ridden.

For a region, RIMS II typically delivers a set of 38-by-471 tables of multipliers

for output, earnings, and employment; supplementary multipliers for taxes are available

at additional cost. Although the model’s documentation is generally excellent, use of

RIMS II alone will not provide proper estimates of a region’s economic impacts from a

change in regional demand. This is because no RPC estimates are supplied with the

model. For example, in order to estimate the impacts of rehabilitation, one not only needs

to be able to convert the engineering cost estimates into demands for labor as well as for

materials and services by industry, but must also be able to estimate the percentage of the

labor income, materials, and services which will be provided by the region’s households

and industries (the RPCs for the demanded goods and services). In most cases, such

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percentages are difficult to ascertain; however, they are provided in the R/Econ™

I–O and IMPLAN models with simple triggering of an option. Further, it is impossible to

change any of the model’s parameters if superior data are known. This model ought not

to be used for evaluating any project or event where superior data are available or where

the evaluation is for a change in regional demand (a construction project or an event) as

opposed to a change in regional supply (the operation of a new establishment).

Model Results

Detailed total economic impacts for about 500 industries can be calculated for

jobs, labor income, and output from R/Econ™ I–O and IMPLAN only. These two

modeling systems can also provide total impacts as well as impacts at the one- and two-

digit industry levels. RIMS II provides total impacts and impacts on only 38 industries

for these same three measures. Only the manual for R/Econ™ I–O warns about the

problems of interpreting and comparing multipliers and any measures of output, also

known as the value of shipments.

As an alternative to the conventional measures and their multipliers, R/Econ™ I–

O and IMPLAN provide results on a measure known as “value added.” It is the region’s

contribution to the nation’s gross domestic product (GDP) and consists of labor income,

nonmonetary labor compensation, proprietors’ income, profit-type income, dividends,

interest, rents, capital consumption allowances, and taxes paid. It is, thus, the region’s

production of wealth and is the single best economic measure of the total economic

impacts of an economic disturbance.

In addition to impacts in terms of jobs, employee compensation, output, and value

added, IMPLAN provides information on impacts in terms of personal income, proprietor

income, other property-type income, and indirect business taxes. R/Econ™ I–O breaks

out impacts into taxes collected by the local, state, and federal governments. It also

provides the jobs impacts in terms of either about 90 or 400 occupations at the users

request. It goes a step further by also providing a return-on-investment-type multiplier

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measure, which compares the total impacts on all of the main measures to the total

original expenditure that caused the impacts. Although these latter can be readily

calculated by the user using results of the other two modeling systems, they are rarely

used in impact analysis despite their obvious value.

In terms of the format of the results, both R/Econ™ I–O and IMPLAN are

flexible. On request, they print the results directly or into a file (Excel® 4.0, Lotus 123®,

Word® 6.0, tab delimited, or ASCII text). It can also permit previewing of the results on

the computer’s monitor. Both now offer the option of printing out the job impacts in

either or both levels of occupational detail.

RSRC Equation

The equation currently used by RSRC in estimating RPCs is reported in Treyz

and Stevens (1985). In this paper, the authors show that they estimated the RPC from the

1977 CTS data by estimating the demands for an industry’s production of goods or

services that are fulfilled by local suppliers (LS) as

LS = De(-1/x) and where for a given industry x = k Z1a1Z2a2 Pj Zjaj and D is its total local demand. Since for a given industry RPC = LS/D then ln{-1/[ln (lnLS/ lnD)]} = ln k + a1 lnZ1 + a2 lnZ2 + Sj ajlnZj which was the equation that was estimated for each industry.

This odd nonlinear form not only yielded high correlations between the estimated

and actual values of the RPCs, it also assured that the RPC value ranges strictly between

0 and 1. The results of the empirical implementation of this equation are shown in Treyz

and Stevens (1985, table 1). The table shows that total local industry demand (Z1), the

supply/demand ratio (Z2), the weight/value ratio of the good (Z3), the region’s size in

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square miles (Z4), and the region’s average establishment size in terms of employees for

the industry compared to the nation’s (Z5) are the variables that influence the value of the

RPC across all regions and industries. The latter of these maintain the least leverage on

RPC values.

Because the CTS data are at the state level only, it is important for the purposes of

this study that the local industry demand, the supply/demand ratio, and the region’s size

in square miles are included in the equation. They allow the equation to extrapolate the

estimation of RPCs for areas smaller than states. It should also be noted here that the CTS

data only cover manufactured goods. Thus, although calculated effectively making them

equal to unity via the above equation, RPC estimates for services drop on the

weight/value ratios. A very high weight/value ratio like this forces the industry to meet

this demand through local production. Hence, it is no surprise that a region’s RPC for this

sector is often very high (0.89). Similarly, hotels and motels tend to be used by visitors

from outside the area. Thus, a weight/value ratio on the order of that for industry

production would be expected. Hence, an RPC for this sector is often about 0.25.

The accuracy of CUPR’s estimating approach is exemplified best by this last

example. Ordinary location quotient approaches would show hotel and motel services

serving local residents. Similarly, IMPLAN RPCs are built from data that combine this

industry with eating and drinking establishments (among others). The results of such

aggregation process is an RPC that represents neither industry (a value of about 0.50) but

which is applied to both. In the end, not only is the CUPR’s RPC-estimating approach the

most sound, but it is also widely acknowledged by researchers in the field as being state

of the art.

Advantages and Limitations of Input-Output Analysis

Input-output modeling is one of the most accepted means for estimating economic

impacts. This is because it provides a concise and accurate means for articulating the

interrelationships among industries. The models can be quite detailed. For example, the

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current U.S. model currently has more than 500 industries representing many six-digit

North American Industrial Classification System (NAICS) codes. The CUPR’s model

used in this study has 517 sectors. Further, the industry detail of input-output models

provides not only a consistent and systematic approach but also more accurately assesses

multiplier effects of changes in economic activity. Research has shown that results from

more aggregated economic models can have as much as 50 percent error inherent in

them. Such large errors are generally attributed to poor estimation of regional trade flows

resulting from the aggregation process.

Input-output models also can be set up to capture the flows among economic

regions. For example, the model used in this study can calculate impacts for a county as

well as the total New Jersey state economy.

The limitations of input-output modeling should also be recognized. The approach

makes several key assumptions. First, the input-output model approach assumes that

there are no economies of scale to production in an industry; that is, the proportion of

inputs used in an industry’s production process does not change regardless of the level of

production. This assumption will not work if the technology matrix depicts an economy

of a recessional economy (e.g., 1982) and the analyst is attempting to model activity in a

peak economic year (e.g., 1989). In a recession year, the labor-to-output ratio tends to be

excessive because firms are generally reluctant to lay off workers when they believe an

economic turnaround is about to occur.

A less-restrictive assumption of the input-output approach is that technology is

not permitted to change over time. It is less restrictive because the technology matrix in

the United States is updated frequently and, in general, production technology does not

radically change over short periods.

Finally, the technical coefficients used in most regional models are based on the

assumption that production processes are spatially invariant and are well represented by

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the nation’s average technology. In a region as large and diverse as New Jersey, this

assumption is likely to hold true.

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APPENDIX C: ECONOMIC IMPACTS OF COMBINED LATTICE-MONOPOLE SCENARIO

Lattice Towers Monopole Towers Combination

249 Towers Per Tower 50% Lattice 249 Towers Per Tower50%

Monopole 50% Each Expenditures in NJ 292,305,381.9 1,173,917.2 146,152,691 337,510,475.3 1,355,463.8 168,755,238 314,907,929 Total Expenditures 397,082,336.1 1,594,708.2 198,541,168 497,893,589.7 1,999,572.6 248,946,795 447,487,963 Employment 2,083.6 8.4 1,042 2,600.1 10.4 1,300 2,342

Direct 1,211.6 4.9 606 1,599.6 6.4 800 1,406 Indirect 872.0 3.5 436 1,000.4 4.0 500 936

Income ($000) 223,476.8 897.5 111,738.4 249,751.3 1,003.0 124,875.7 236,614 GDP ($000) 288,104.3 1,157.0 144,052.2 320,077.6 1,285.5 160,038.8 304,091 Roseland Switching Station Expenditures in NJ 57,074,195.0 57,074,195.0 57,074,195.0 Total Expenditures 166,613,772.0 166,613,772.0 166,613,772.0 Employment 592 592 592

Direct 462 462 462 Indirect 130 130 130

Income ($000) 39,776 39,766 39,766 GDP ($000) 51,115 51,115 51,115 Jefferson Switching Station Expenditures in NJ 62,089,015.0 62,089,015.0 62,089,015.0 Total Expenditures 77,000,000.0 77,000,000.0 77,000,000.0 Employment 739 739 739

Direct 584 584 584 Indirect 154 154 154

Income ($000) 44,228.2 44,228.2 44,228.2 GDP ($000) 56,929.2 56,929.2 56,929.2

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Total Expenditures & Impacts Expenditures in NJ 411,468,592 456,673,685 434,071,139 Total Expenditures 640,696,108 741,507,362 691,101,735 Management Reserve 8,492,638 8,492,638 8,492,638 Total Budget 649,188,746 750,000,000 699,594,373 Employment 3,414 3,931 3,672

Direct 2,258 2,646 2,452 Indirect 1,156 1,285 1,220

Income ($000) 307,481 333,746 320,608 GDP ($000) 396,148 428,122 412,135