Economic Impact of The NYC Food Processing and ... · B. Bergen, Essex, Hudson, Middlesex,...

Economic Impact of

The NYC Food Processing and Distribution Center Development Project

Prepared for:

New York City Regional Center

Prepared by:

Michael K. Evans

Evans, Carroll & Associates, Inc.

2785 NW 26th

St.

Boca Raton, FL 33434

561-470-9035

[email protected]

May 22, 2014

mailto:[email protected]

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Table of Contents

1. Executive Summary 3 2. Tabulation of Principal Results 6 3. Introduction and Scope of Work 9 4. Detailed Industry Results of Construction Expenditures 10 5. Brief Guide to RIMS II Input/Output Model 12 6. Methodology for Calculating Indirect Job Gains 15 7. Economic Parameters for 16 New York City Metropolitan Area Counties 20

A. New York (Manhattan), Bronx, Kings, Queens, Nassau, Westchester, Richmond, Suffolk, and Rockland 21

B. Bergen, Essex, Hudson, Middlesex, Monmouth, Morris, Union 39

C. Commuting Patterns for Bronx County 49 Appendix: Resume of Dr. Michael K. Evans 51

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1. Executive Summary

This report presents a preliminary estimate of the number of jobs created for

The NYC Food Processing and Distribution Center Development Project (the “Project”), which is located in the borough of the Bronx, New York.

1. The total cost of the Project is $208 million. This funding includes $85 million of EB-5 funding and $123 million from Fresh Direct Holdings, Inc. (“Fresh Direct”), the borrower of EB-5 funds and the developer of the Project. Of this amount, $192,832,000 represents EB-5 qualifying expenditures.

2. Since the Project construction period is greater than 24 months, only construction related jobs will be calculated.

3. The governments of the City and State of New York have noted that redevelopment within the Bronx is an important source of jobs and economic development activity for New York City. The Bronx currently has the highest unemployment rate of any county in the state of New York. A component of bringing new jobs to the Bronx is the continued redevelopment of the Harlem River Yard Transportation and Distribution Center located at the southern tip of the Bronx. This multi-modal transportation park is owned by the State of New York and provides warehousing, distribution, and related freight services to businesses serving the New York City metro area.

4. The Project involves the construction of a new state-of-the-art, 424,000 square foot food processing and distribution center, one of the largest in New York City, within the Harlem River Yard Transportation and Distribution Center. The new facility will include space for food processing, warehousing, and distribution as well as corporate offices for Fresh Direct the largest independent, full service internet food grocer in the United States. The Project is an economic development initiative that will bring new jobs to the Bronx. The Project is also a component of New York City’s efforts to strengthen and grow the food manufacturing and distribution industry, a significant sector of the city’s economy. The Project involves the governments of the City of New York and the State of New York, as well as Fresh Direct.

5. The calculation of the number of permanent new jobs created by this Project are based on the RIMS II final demand multiplier for the 16-county area consisting of New York (Manhattan), Bronx, Kings, Queens, Richmond, Suffolk, Westchester, Nassau, and Rockland counties in New York State, and Bergen, Essex, Hudson, Middlesex, Monmouth, Morris, and Union counties in New Jersey (the “Study Region”).

6. The RIMS II final demand multiplier for construction in the Study Region is 12.3936. The total EB-5 qualifying construction cost budget is expected to be $192,832,000. However, the construction expenditures are in current 2014 dollars, whereas the input/output data and multipliers are based on 2010 dollars. Hence the construction figure should be deflated by the expected increase in the cost of construction from 2010 through 2014. According to the Turner Construction Cost Index, described further in

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Section 4, construction costs will have risen 11.4% from 2010 through 2014, so the $192,832,000 figure is deflated by 1.114 to obtain about $173.098 million. This figure is then multiplied by 12.3936 to obtain a total of 2,145 total new jobs. Since the maximum amount of the EB-funding is $85 million, and the Project is located in a Targeted Employment Area, which means each immigrant may invest $500,000, a total of 170 investors will be sought. Accordingly, a total of at least 1,700 new jobs must be created. Thus, an excess of 445 new jobs (26% surplus) will be created in this Project.

7. The EB-5 qualifying construction cost budget has been provided by the New York City Regional Center (“NYCRC”). NYCRC received the construction cost data directly from Fresh Direct and its general contractor, Shimenti Construction Company.

8. Fresh Direct is the largest independent, full service internet food grocer in the United States. The company is a direct-to-consumer online food and grocery provider that delivers fresh food and brand-name groceries to its customers’ door. Rather than travel to a grocery store, Fresh Direct customers are able to place orders online and receive the items the next day. Fresh Direct currently processes up to 14,000 orders per day and its fleet of over 250 trucks delivers to over 75,000 households per week in the New York City and Philadelphia metropolitan areas. More than 750,000 households have used Fresh Direct to date and it widely recognized as the premier internet food grocer in New York City, the largest grocery retail market in America. Founded in 1999, Fresh Direct currently employs over 2,500 individuals and its 2013 sales were $441 million. Fresh Direct projected sales for 2014 are $500 million. The last equity round of funding, completed in March 2014, valued the company at over $550 million.

Fresh Direct possesses professional in-house construction capability and has spent considerable time and resources on preparing and finalizing construction costs expenditures of the Project. Fresh Direct’s Vice President of Facility Design and Development, Richard J. Leal, has over 30 years of construction management and procurement experience, with a particular emphasis on ground-up construction. Mr. Leal is an engineer who has overseen construction projects for entities such as the large public utility Public Service Electric and Gas Company (PSE&G), as well as a 250-store retailer and a leading privately held real estate development company in New York City. Accordingly, Fresh Direct has the depth of experience and financial expertise to provide construction expenditures that are reliable, precise, and credible.

9. In addition, the general contractor hired by Fresh Direct to oversee construction of the Project is Schimenti Construction Company (“Schimenti Construction”). Based in Ridgefield, Connecticut and New York City, Schimenti Construction is a building construction company offering diversified general construction and design-build services to a variety of clients, which include some of the largest retailers in the United States. Schimenti Construction has been overseeing projects in New York City for over 20 years. The company staffs over 100 trade professionals who coordinate all aspects of construction management using a proprietary system developed for its clients. Schimenti Construction offers general contracting, pre-construction planning, and comprehensive project management services, including the planning and scheduling of

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the manpower, equipment, materials, and subcontractors required for a project. Schimenti Construction has served as a general construction contractor for the following leading retail companies: Starbucks; Target; The Home Depot; Best Buy; Bed Bath & Beyond; GAP; Bank of America; Barnes & Noble; Victoria’s Secret; and Banana Republic.

Because of its extensive construction management experience, Schimenti Construction possesses professional construction cost and schedule-estimating capabilities and has spent considerable time and resources on preparing and finalizing construction cost estimates and construction timelines for the Project. Accordingly, information provided by Schimenti Construction is from a highly reliable, precise and credible source.

10. As outlined above, Fresh Direct and Schimenti Construction possess the requisite expertise and background to accurately generate the EB-5 qualifying construction budget produced specifically for this economic report. We thus confirm that the job creation totals are based on applicable, reliable and up-to-date information.

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2. Tabulation of Principal Results

The results for the employment multipliers for the construction of the Project are summarized in Table A. The RIMS II final demand multipliers include the direct as well as the indirect and induced effects for the construction of the Project, but no operations jobs are included. All figures in Table A represent permanent new jobs created.

Table A. Summary of Employment and Revenue

Activity Expenditures/ Final Demand Total

Revenues Multiplier Jobs

(mil 2010 $) Activity

Construction * 173.098 12.3936 2145.3

*All figures calculated from unrounded numbers

Table B-1 shows the NAICS codes used in this report and the definitions from the NAICS code manual. Table B-2 contains print screen images of the exact multipliers used in this study taken from Table 1-5 of the RIMS II input/output multiplier tables.

Table B-1. NAICS Codes and Definitions 236220 Commercial and Institutional Building Construction

Table B-2. Print Screen Multipliers and List of Counties (1) (2) (3) (4) (5) (6) 230000 Construction 1.9074 0.5925 12.3936 1.0426 1.7034 1.8509

http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=236&search=2007%20NAICS%20Search

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The economic impact of construction expenditures, as measured by household earnings, demand for business services, utilities, maintenance and repair, and new supplier and vendor relationships is summarized in Table C.

Table C. Summary Measures of Economic Impact for Project

All figures in thousands of dollars

Household Income from

Construction $102,561

Demand (output) for:

Utilities $2,614

Maintenance and repair construction $917

Supplier/vendor links with manufacturers $19,456

Professional and business support services $25,255

Total these 4 categories $48,242

Household Earnings (Labor Income)

The jobs created by the construction of the Project will subsequently create new

sources of household income. The household income for the total jobs created by construction is about $102.6 million.

This income calculation comes from the RIMS II input-output model, which

measures the average income per job by industry. The model calculations are based on the types of jobs that will be created by the Project, with indirect/induced impacts allocated based on the types of commodity inputs required by the businesses that would directly or indirectly provide goods and services to the Project.

The details used to calculate these figures are given throughout the report. Separate tables are provided for the total number of jobs created, the average earnings per new worker, and the total increase in earnings for construction of the Project. In each case, the RIMS II input/output model has been used to calculate the number of jobs in each major industrial classification, the average earnings per employee, and hence total earnings. The number of jobs by industrial classification is based on calculations imbedded in the RIMS II model for each of the activities as summarized in Table A and documented in detail throughout this report

Demand for Business Services, Utilities, Maintenance and Construction, and New Supplier/Vendor Relationships Created with Manufacturers

The total economic impact of the Project from the supplier purchases and business relationships for the Project will create approximately $48.2 million in

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additional economic activity across the region. These supplier purchases are calculated from the indirect increase in output generated by the RIMS II model. The estimate of supplier purchases is based on the commodity data in the RIMS II input-output model. This data specifies the amount and type of commodity input needed to maintain specific types of business operations. The model estimates the supplier purchases based on the types of jobs and number of jobs that will be created by the Project. In addition, the model allocates the supplier purchases to businesses within the region, based on trade flow data from the U.S. Bureau of Economic Analysis. Utilities include services such as electricity, natural gas, and water and sewer facilities. The economic impact on utility services totals about $2.6 million. Maintenance and repair services include some building and construction activity after the Project is completed. The Project would create an economic impact of about $0.9 million within these sectors in the region. These are permanent, ongoing repairs and do not include the original construction. Because the building will be new, the economic impact for construction sectors is minimal on an ongoing basis. New supplier/vendor relationships with manufacturers would create an economic impact of about $19.5 million. This output represents purchases of locally manufactured goods for construction. The Project will also create demand for various types of business services, including professional and scientific services, management of companies, administrative services, and building support and waste management services. The impact of this activity totals about $25.3 million. This represents payments to architects, engineers, and other professional and business services for the construction activities.

The figures given in Table C represent only a brief summary of the detailed calculations that have been undertaken and are reported in tabular format throughout the report. The figure for utility output, for example, represents the sum of utility output for each of the categories of economic activity listed in Table A. For maintenance and repair construction, this figure represents the amount spent times the input/output coefficient showing the total amount of output per $1 million of construction expenditures. The same methodology applies to all the other figures given in Table C.

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3. Introduction and Scope of Work

The governments of the City and State of New York have noted that redevelopment within the Bronx is an important source of jobs and economic development activity for New York City. The Bronx currently has the highest unemployment rate of any county in the state of New York. A component of bringing new jobs to the Bronx is the continued redevelopment of the Harlem River Yard Transportation and Distribution Center located at the southern tip of the Bronx. This multi-modal transportation park is owned by the State of New York and provides warehousing, distribution, and related freight services to businesses serving the New York City metro area.

The Project involves the construction of a new state-of-the-art, 424,000 square foot food processing and distribution center, one of the largest in New York City, within the Harlem River Yard Transportation and Distribution Center. The new facility will include space for food processing, warehousing, and distribution as well as corporate offices for Fresh Direct, the largest full service internet food grocer in the United States. The Project is an economic development initiative that will bring new jobs to the Bronx. The Project is also a component of New York City’s efforts to strengthen and grow the food manufacturing and distribution industry, a significant sector of the city’s economy. The Project involves the governments of the City of New York and the State of New York, as well as Fresh Direct.

Section (4) contains the detailed industry results of the construction expenditures. The increase in employment, output, and earnings, and the level of output and earnings per new worker are shown for the 20 major industrial classifications in the RIMS II model. Section (5) of the report contains a brief description of the RIMS II model; Section (6) offers a more detailed explanation of how the indirect jobs are calculated in regional input/output models. Section (7) contains key economic indicators for employment, income distribution, labor markets, and the level and growth rate of population and personal income for the 16 counties that comprise this Study Region, and the commuting patterns for Bronx County.

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4. Detailed Industry Results of Construction Expenditures

The two tables in this section show the detailed industry results for the $173.098 million in EB-5 qualifying construction expenditures for this Project. Since the Project will last more than two years, direct as well as indirect and induced effects may be included.

Table 4-1. Increase in Employment, Output, and Earnings for Construction Expenditures

Industry Group Employment Output Earnings

Agriculture, forestry, fishing, 0.7 69 17

Mining 0.8 208 35

Utilities 4.1 2,614 450

Construction 1165.1 174,015 60,532

Manufacturing 65.8 19,456 3,479

Wholesale trade 43.9 10,871 3,150

Retail trade 174.3 15,302 4,881

Transportation and warehousing 37.4 5,158 1,748

Information 28.5 10,074 1,956

Finance and insurance 79.7 18,781 5,037

Real estate and rental and leasing 85.6 22,260 1,610

Professional and scientific services 89.2 15,994 6,526

Management of companies 11.1 3,774 1,194

Admin and waste mgmt services 81.1 5,487 2,198

Educational services 17.3 1,437 554

Health care and social assistance 109.7 11,476 5,141

Arts, entertainment, and recreation 19.0 1,454 502

Accommodation 12.9 1,489 433

Food services and drinking places 59.3 3,774 1,160

Other services 43.4 6,474 1,835

Household 16.4 0 121

Total 2145.3 330,167 102,561

Table 4-1 shows that the construction expenditures as described earlier in this report that would create approximately 2,145 new jobs. Total output will rise about $330.2 million, with household earnings up by approximately $102.6 million. Table 4-2 shows that average output per new worker will be about $153,900, with average annual earnings of about $47,800. For construction workers, the comparable statistics are $149,400 for output and $52,000 for average annual earnings.

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Table 4-2. Output and Earnings Per New Worker for $173.098 Million of Construction Expenditures

Industry Group Employment Output/Empl Earnings/Empl

Agriculture, forestry, fishing, 0.7 100.0 25.0

Mining 0.8 260.9 43.5

Utilities 4.1 642.6 110.6

Construction 1165.1 149.4 52.0

Manufacturing 65.8 295.6 52.9

Wholesale trade 43.9 247.5 71.7

Retail trade 174.3 87.8 28.0

Transportation and warehousing 37.4 138.1 46.8

Information 28.5 353.6 68.7

Finance and insurance 79.7 235.6 63.2

Real estate and rental and leasing 85.6 260.2 18.8

Professional and scientific services 89.2 179.3 73.1

Management of companies 11.1 338.5 107.1

Admin and waste mgmt services 81.1 67.7 27.1

Educational services 17.3 83.1 32.0

Health care and social assistance 109.7 104.6 46.9

Arts, entertainment, and recreation 19.0 76.4 26.4

Accommodation 12.9 115.6 33.6

Food services and drinking places 59.3 63.7 19.6

Other services 43.4 149.1 42.3

Household 16.4 0.0 7.4

Total 2145.3 153.9 47.8

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5. Brief Guide to RIMS II Input/Output Model The following material has been condensed from the RIMS II User Handbook.

Introduction and General Comments

Effective planning for public- and private-sector projects and programs at the State and local levels requires a systematic analysis of the economic impacts of these projects and programs on affected regions. In turn, systematic analysis of economic impacts must account for the inter-industry relationships within regions because these relationships largely determine how regional economies are likely to respond to project and program changes. Thus, regional input-output (I-O) multipliers, which account for inter-industry relationships within regions, are useful tools for conducting regional economic impact analysis. In the 1970s, the Bureau of Economic Analysis (BEA) developed a method for estimating regional I-O multipliers known as RIMS (Regional Industrial Multiplier System), which was based on the work of Garnick and Drake. In the 1980s, BEA completed an enhancement of RIMS, known as RIMS II (Regional Input-Output Modeling System), and published a handbook for RIMS II users. In 1992, BEA published a second edition of the handbook in which the multipliers were based on more recent data and improved methodology. In 1997, BEA published a third edition of the handbook that provides more detail on the use of the multipliers and the data sources and methods for estimating them.

RIMS II is based on an accounting framework called an I-O table. For each industry, an I-O table shows the industrial distribution of inputs purchased and outputs sold. A typical I-O table in RIMS II is derived mainly from two data sources: BEA's national I-O table, which shows the input and output structure of nearly 500 U.S. industries, and BEA's regional economic accounts, which are used to adjust the national I-O table to show a region's industrial structure and trading patterns.

Using RIMS II for impact analysis has several advantages. RIMS II multipliers can be estimated for any region composed of one or more counties and for any industry, or group of industries, in the national I-O table. The accessibility of the main data sources for RIMS II keeps the cost of estimating regional multipliers relatively low. Empirical tests show that estimates based on relatively expensive surveys and RIMS II-based estimates are similar in magnitude.

BEA's RIMS multipliers can be a cost-effective way for analysts to estimate the economic impacts of changes in a regional economy. However, it is important to keep in mind that, like all economic impact models, RIMS provides approximate order-of-magnitude estimates of impacts. RIMS multipliers are best suited for estimating the impacts of small changes on a regional economy. For some applications, users may want to supplement RIMS estimates with information they gather from the region

http://www.bea.gov/scb/pdf/regional/perinc/meth/rims2.pdf

http://www.bea.gov/scb/pdf/regional/perinc/meth/rims2.pdf

http://www.bea.gov/scb/pdf/national/inputout/meth/io1992.pdf

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undergoing the potential change. To use the multipliers for impact analysis effectively, users must provide geographically and industrially detailed information on the initial changes in output, earnings, or employment that are associated with the project or program under study. The multipliers can then be used to estimate the total impact of the project or program on regional output, earnings, and employment.

RIMS II is widely used in both the public and private sector. In the public sector, for example, the Department of Defense uses RIMS II to estimate the regional impacts of military base closings. State transportation departments use RIMS II to estimate the regional impacts of airport construction and expansion. In the private-sector, analysts and consultants use RIMS II to estimate the regional impacts of a variety of projects, such as the development of shopping malls and sports stadiums.

RIMS II Methodology

RIMS II uses BEA's benchmark and annual I-O tables for the nation. Since a particular region may not contain all the industries found at the national level, some direct input requirements cannot be supplied by that region's industries. Input requirements that are not produced in a study region are identified using BEA's regional economic accounts. The RIMS II method for estimating regional I-O multipliers can be viewed as a three-step process. In the first step, the producer portion of the national I-O table is made region-specific by using six-digit NAICS location quotients (LQs). The LQs estimate the extent to which input requirements are supplied by firms within the region. RIMS II uses LQs based on two types of data: BEA's personal income data (by place of residence) are used to calculate LQs in the service industries; and BEA's wage-and-salary data (by place of work) are used to calculate LQs in the non-service industries. In the second step, the household row and the household column from the national I-O table are made region-specific. The household row coefficients, which are derived from the value-added row of the national I-O table, are adjusted to reflect regional earnings leakages resulting from individuals working in the region but residing outside the region. The household column coefficients, which are based on the personal consumption expenditure column of the national I-O table, are adjusted to account for regional consumption leakages stemming from personal taxes and savings. In the last step, the Leontief inversion approach is used to estimate multipliers. This inversion approach produces output, earnings, and employment multipliers, which can be used to trace the impacts of changes in final demand on and indirectly affected industries.

Advantages of RIMS II

There are numerous advantages to using RIMS II. First, the accessibility of the main data sources makes it possible to estimate regional multipliers without conducting relatively expensive surveys. Second, the level of industrial detail used in RIMS II helps

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avoid aggregation errors, which often occur when industries are combined. Third, RIMS II multipliers can be compared across areas because they are based on a consistent set of estimating procedures nationwide. Fourth, RIMS II multipliers are updated to reflect the most recent local-area wage-and-salary and personal income data.

Overview of Different Multipliers

RIMS II provides users with five types of multipliers: final demand multipliers for output, for earnings, and for employment; and direct-effect multipliers for earnings and for employment. These multipliers measure the economic impact of a change in final demand, in earnings, or in employment on a region’s economy. The final demand multipliers for output are the basic multipliers from which all other RIMS II multipliers are derived. In this table, each column entry indicates the change in output in each row industry that results from a $1 change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multiplier for each row. The total impact on regional output is calculated by multiplying the final demand change in the column industry by the sum of all the multipliers for each row except the household row.

RIMS II provides two types of multipliers for estimating the impacts of changes on earnings: final demand multipliers and direct effect multipliers. These multipliers are derived from the table of final demand output multipliers.

The final demand multipliers for earnings can be used if data on final demand changes are available. In the final demand earnings multiplier table, each column entry indicates the change in earnings in each row industry that results from a $1 change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multipliers for each row. The total impact on regional earnings is calculated by multiplying the final demand change in the column industry by the sum of the multipliers for each row.

Employment Multipliers

RIMS II provides two types of multipliers for estimating the impacts of changes on employment: final demand multipliers and direct effect multipliers. These multipliers are derived from the table of final demand output multipliers. The final demand multipliers for employment can be used if the data on final demand changes are available. In the final demand employment multiplier table, each column entry indicates the change in employment in each row industry that results from a $1 million change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multiplier for each row. The total impact on regional employment is calculated by

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multiplying the final demand change in the column industry by the sum of the multipliers for each row.

The direct effect multipliers for employment can be used if the data on the initial changes in employment by industry are available. In the direct effect employment multiplier table, each entry indicates the total change in employment in the region that results from a change of one job in the row industry. The total impact on regional employment is calculated by multiplying the initial change in employment in the row industry by the multiplier for the row.

Choosing a Multiplier

The choice of multiplier for estimating the impact of a project on output, earnings, and employment depends on the availability of estimates of the initial changes in final demand, earnings, and employment. If the estimates of the initial changes in all three measures are available, the RIMS II user can select any of the RIMS II multipliers. In theory, all the impact estimates should be consistent. If the available estimates are limited to initial changes in final demand, the user can select a final demand multiplier for impact estimation. If the available estimates are limited to initial changes in earnings or employment, the user can select a direct effect multiplier.

6. Methodology for Calculating Indirect Job Gains

In spite of the explanation of the RIMS II model given directly above, some USCIS adjudicators have asked for further clarification about how that model is used to determine the increase in the number of indirect jobs. That is an important issue because, unlike the direct job count, which can be verified by USCIS from various payroll and withholding documents, the calculation of indirect jobs cannot be verified directly but depends on mathematical calculations. The general concept is based on the coefficients in the input/output model itself (the same methodology applies to RIMS II, IMPLAN, or any other generally recognized and accepted input/output model). In any given year, the government calculates how much input is used for a given production of output. The detailed figures are taken from the Economic Censuses taken once every five years; the figures are then updated from various annual supplements. Basically the process has two steps, each of which is described next in greater detail. The first is to determine the amount of output, and hence the number of jobs, required to produce a given amount (say $1 million) of the final product or service. These are national coefficients. The second is to determine what proportion of those goods and services are purchased within the local region (the regional purchase coefficients, or RPCs).

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In the case of a manufacturing process, the national coefficients are based on production functions: how much coke per ton of steel, how much steel per motor vehicle, how much flour for a loaf of bread, and so on. However, most of the jobs are created in the service sector, where Commerce Department data are used to determine, for example, how much restaurants spend on laundry services, how much airlines spend for attorneys, and so on. These figures are based on information contained in the various Economic Censuses. The national coefficients would also determine, for example, how many architects and engineers would be hired for a construction project of a given scope and size, and how many new employees at financial institutions would be required to handle the additional cash flow generated by the new business. Both of these are discussed below in greater detail. Even after these coefficients are determined, however, the regional purchase coefficients (RPC) must still be estimated. If, for example, a trucking firm spends 1% of its revenue on accountants, how much of that money is spent on local firms, and how much is spent outside the region? That answer depends on various factors. The most important is the amount of the good or service produced within the region. If a trucking firm, for example, were located in a small county with no accountants, obviously it would not spend any of that money locally. That sets a lower limit but is not generally the case. Instead, a balancing algorithm is used.

Suppose, for example, that all the firms producing, distributing, or selling goods and services in a given county spent $10 million on accounting services. Also, suppose that total billings of all accountants in the county were $20 million. In that case, local accountants could handle all the local business, plus business from neighboring counties. If, on the other hand, total accountant billings in the county were only $5 million, local firms could not spend more than half of the money on local accountants.

Of course it is possible that there are adequate resources in the county but local firms choose to use companies outside the county; perhaps prices or service is better. No input/output model can account for such anomalies. On the other hand, given transportation costs, it would be highly unusual for a firm to be located in a given location and not serve the nearby businesses, instead choosing only those clients who were farther away. The RIMS II model – and other regional input/output models – assigns regional purchase coefficients (RPCs) in all cases where the local industry purchases goods and services from local firms. This matrix could have as many as 406 * 406 = 164,836 elements, although in practice many of them are zero. Large counties with a wide variety of businesses have more non-zero elements than small counties with relatively few businesses. In general, the RPCs tend to be close to zero for most manufactured goods, and close to unity for most services. While there are many exceptions to this rule, most firms

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will use financial, professional, business, and health care services that are located in that county or contiguous areas. To take just one example of many, consider the number of new jobs created by architects and engineers for a new construction project of any given size. Most construction cost manuals, such as those published by R. S. Means, indicate that those costs are usually about 5% to 9% of the total job. According to the national input/output file, the figures are 9.2% for commercial construction and 4.5% for industrial construction. These figures are fairly typical of other locations and regions; except for “signature” buildings designed by famous names, most architects and engineers live in the same region as the buildings that are being constructed. To summarize to this point, the number of indirect jobs as a proportion of direct jobs depends on (a) the national relationships, and (b) the regional purchase coefficients. In our presentation for the businesses in this report, we provide further discussion of those industries with the largest number of indirect jobs. However, there are a few industries that produce relatively large numbers of jobs in almost all cases, and these can be generally discussed at this stage in order to avoid repeating this information several times. The industries discussed here include banking, real estate, legal and accounting, architects and engineers, other professional services, employment services, other business services, restaurants, and government. In all of these cases, the vast majority of workers are hired locally. Our comments for the rest of this section are based on the assumption of a $10 million investment; the results are linear. Banking and credit: On an aggregate basis, for every $10 million in deposits, very broadly defined (M3), there is about 1 new banking employee. As a rough rule of thumb, the size of M3 is roughly equal to the size of GDP. Hence we would expect about 1 new banking employee for every $10 million increase in output, as calculated from the RIMS II model.

Real estate: Additional real estate employees are based on two factors. One is the leasing activity of the new building, and the other is the increase in residential real estate activity as people get new jobs, either within the area or by moving into the area. On a lease basis, a $10 million investment is likely to result in a building of 80,000 square feet. If it leases for $40/square foot, that would be $3.2 million in annual lease payments, and with a 6% commission would generate $192,000 in revenues, which would account for about 2 new real estate employees (the figure would be less for industrial buildings). The increase in employment would also result in some real estate activity as workers moved into better housing in the same location, or moved in from other areas. In a normal year, there are about 7 million sales of new and existing homes for a labor force of about 140 million, or 5%. Hence if the total increase in employment were 200, that would imply 10 real estate transactions; if they average $200,000 at a 6% commission, that would be $12,000 per home or a total of $120,000, which would support approximately 1 new real estate job.

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Legal & Accounting: Each of these accounts for about 1% of total employment; so if there were a total increase of 200 jobs, we would expect an average of 4 new employees in this classification.

Architects & Engineers: almost all of these jobs stem from the new construction

activity. This category has already been discussed above; for a $10 million construction project, which would create about 80 new construction jobs, we would expect about 7 new jobs in architects and engineers for a commercial project and 3 to 4 new jobs for an industrial project.

Other professional services: This category includes employees in consulting, scientific research and development, advertising, and management, as well as several other smaller, specialized categories. In general, consulting, management, and the all other category each account for about 1% of total employment, and R&D and advertising account for about ½% of total employment, for a total of about 4% of total employment. This figure will vary widely depending on the degree to which consultants and R&D are used by the new business. Employment services: On a national average basis, 1 out of every 45 people is employed by this industry. Here again, the figures will vary widely depending on (a) the proportion of people who are hired through employment agencies, and (b) the proportion of the work that is outsourced to employment services. Business support services include office management, travel arrangement, security, credit bureaus, telemarketing, and back-office jobs that are outsourced, such as direct mail, copying, and duplicating services. The back-office services would vary widely depending on the type of new business; retail stores, for example, would print and distribute more advertising brochures than a manufacturing operation. On a national average basis, these jobs account for about 2% of total employment. Building support services, which includes janitorial services, lawn maintenance, and waste management. For an office building of 80,000 square feet, the cost would be approximately $2/sq ft per year for maintenance, or $160,000, which would support about 4 new jobs; here again, the figure would be lower for industrial buildings.

Restaurants: This category reflects business meals. Of course the number of business meals depends greatly on the type of business; lawyers, accountants, and consultants will have more business meals than manufacturing plants or water treatment facilities. On a national average basis, Commerce Department figures show that total restaurant sales in 2007 were $580 billion, while consumer expenditures at restaurants were $500 billion. However, that figure also includes tips, which are not included in restaurant sales. After subtracting 15% for tips, that indicates about $425 billion in food and beverage purchases by consumers, indicating about $155 billion for business expenses. With a labor force of approximately 140 million, that is equivalent to about $1,100 per employee. Hence if 200 new jobs were created, business meal expenses would rise an average of $221,000, which would imply about 4.5 new indirect

19

jobs in the restaurant industry. These figures are likely to be somewhat higher when direct jobs are created for office buildings and hotels.

Government: The increase in public sector employees represents the amount funded by increased real estate taxes. For a construction project with $10 million in hard costs, the total value is likely to be between $15 and $20 million when one includes furniture, fixtures, equipment, and land values. Using a national average property tax rate of 1%, that would raise $150,000 to $200,000, which would create 3 to 4 new jobs in the public sector.

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7. Economic Parameters for 16 Counties in the New York Metropolitan Area

This section contains information about employment, income distribution, labor market parameters, the level and growth rate of population and personal income, and commuting patterns around New York City for 16 counties in the New York metropolitan area. This list is similar to, but somewhat smaller than, the NYC metro area as defined by Census, and is substantially smaller than the 30-county Consolidated Statistical Area, or the BEA Economic Area for New York City. For ease of exposition we have divided the tables, commentary, and analysis into two sub-groups, which are as follows: A. New York Counties: New York (Manhattan), Bronx, Kings, Queens, Nassau, Westchester, Richmond, Suffolk, and Rockland. B. New Jersey Counties: Bergen, Essex, Hudson, Middlesex, Monmouth, Morris, and Union The analysis thus proceeds as follows. For each subgroup, the following tables are presented: 1. Employment by major industrial classification, income distribution by household and families, mean and median levels of income and poverty rates. In general, there are 2 to 3 counties in each table, so there are anywhere from 1 to 3 tables for each subgroup in this category. 2. Labor market statistics: size of labor force, number of employed and unemployed, and unemployment rate 3. Level and growth rate of population 4. Level and growth rate of personal income This section then concludes with the commuting patterns for Bronx County.

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Group A: New York (Manhattan), Bronx, Kings, Queens, Nassau, Westchester, Richmond, Suffolk, and Rockland

Table 7-1. Key Economic Statistics for New York and Bronx Counties Compared

to the U. S. Economy

New York County,

Bronx County,

United States

Subject Estimate Percent Estimate Percent Estimate Percent

EMPLOYMENT STATUS

Population 16 years and over 1,379,309 100.0% 1,063,397 100.0% 243,832,923 100.0%

In labor force 925,067 67.1% 624,200 58.7% 156,966,769 64.4%

Civilian labor force 924,189 67.0% 623,302 58.6% 155,917,013 63.9%

Employed 839,414 60.9% 524,948 49.4% 139,033,928 57.0%

Unemployed 84,775 6.1% 98,354 9.2% 16,883,085 6.9%

Armed Forces 878 0.1% 898 0.1% 1,049,756 0.4%

OCCUPATION

Civilian employed population 16 + 839,414 100.0% 524,948 100.0% 139,033,928 100.0%

Management, business, science 491,821 58.6% 125,103 23.8% 49,975,620 35.9%

Service occupations 118,282 14.1% 171,799 32.7% 25,059,153 18.0%

Sales and office occupations 175,536 20.9% 125,945 24.0% 34,711,455 25.0%

Construction and maintenance 14,988 1.8% 40,408 7.7% 12,697,304 9.1%

Production, transportation 38,787 4.6% 61,693 11.8% 16,590,396 11.9%

INDUSTRY


Agriculture and mining 815 0.1% 707 0.1% 2,646,975 1.9%

Construction 13,183 1.6% 28,747 5.5% 8,686,813 6.2%

Manufacturing 32,507 3.9% 18,634 3.5% 14,439,691 10.4%

Wholesale trade 17,896 2.1% 10,342 2.0% 3,941,066 2.8%

Retail trade 61,992 7.4% 62,689 11.9% 16,203,408 11.7%

Transportation and utilities 21,740 2.6% 37,729 7.2% 6,843,579 4.9%

Information 52,409 6.2% 10,524 2.0% 3,015,521 2.2%

Finance, insurance, and real estate 135,840 16.2% 37,711 7.2% 9,275,465 6.7%

Professional, scientific, management 156,452 18.6% 44,030 8.4% 14,710,089 10.6%

Education and health care 194,262 23.1% 166,086 31.6% 32,311,107 23.2%

Arts, entertain, hotel and food svcs 90,641 10.8% 55,189 10.5% 12,859,572 9.2%

Other services, except public admin 36,381 4.3% 34,473 6.6% 6,913,449 5.0%

Public administration 25,296 3.0% 18,087 3.4% 7,187,193 5.2%

INCOME AND BENEFITS

Total households 726,090 100.0% 471,912 100.0% 114,567,419 100.0%

Less than $10,000 74,266 10.2% 79,341 16.8% 8,757,190 7.6%

$10,000 to $14,999 36,539 5.0% 43,327 9.2% 6,668,865 5.8%

$15,000 to $24,999 66,380 9.1% 69,249 14.7% 13,165,380 11.5%

$25,000 to $34,999 52,714 7.3% 52,004 11.0% 12,323,322 10.8%

$35,000 to $49,999 70,985 9.8% 68,037 14.4% 16,312,385 14.2%

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$50,000 to $74,999 100,640 13.9% 73,660 15.6% 20,940,859 18.3%

$75,000 to $99,999 69,353 9.6% 39,187 8.3% 13,526,500 11.8%

$100,000 to $149,999 91,391 12.6% 32,185 6.8% 13,544,839 11.8%

$150,000 to $199,999 50,360 6.9% 9,307 2.0% 4,809,998 4.2%

$200,000 or more 113,462 15.6% 5,615 1.2% 4,518,081 3.9%

Median household income (dollars) 63,832 127.5% 32,568 65.1% 50,046 Mean household income (dollars) 119,199 174.6% 45,625 66.8% 68,259

Families 305,679 100.0% 314,618 100.0% 76,089,045 100.0%

Less than $10,000 19,382 6.3% 42,503 13.5% 3,824,251 5.0%

$10,000 to $14,999 12,302 4.0% 24,479 7.8% 2,660,781 3.5%

$15,000 to $24,999 27,974 9.2% 48,536 15.4% 6,770,812 8.9%

$25,000 to $34,999 24,079 7.9% 35,198 11.2% 7,332,318 9.6%

$35,000 to $49,999 29,842 9.8% 46,511 14.8% 10,578,051 13.9%

$50,000 to $74,999 34,271 11.2% 51,090 16.2% 14,990,631 19.7%

$75,000 to $99,999 25,776 8.4% 29,454 9.4% 10,638,931 14.0%

$100,000 to $149,999 37,165 12.2% 24,535 7.8% 11,261,766 14.8%

$150,000 to $199,999 23,884 7.8% 7,736 2.5% 4,130,868 5.4%

$200,000 or more 71,004 23.2% 4,576 1.5% 3,900,636 5.1%

Median family income (dollars) 78,197 129.0% 36,627 60.4% 60,609

Mean family income (dollars) 159,324 200.8% 49,786 62.8% 79,338 Per capita income (dollars) 56,556 217.0% 16,671 64.0% 26,059 PERCENTAGE BELOW POVERTY

All families 12.4% 109.7% 27.6% 244.2% 11.30%

All people 16.4% 107.2% 30.2% 197.4% 15.30%

Please note that in these tables, the percentage figures in regular type refer to the overall category in that column, while the figures in bold are relative to the U.S. average figures

It is not surprising to find that in New York County (Manhattan) the proportion of the workforce engaged in financial services is more than twice the national average, standing at 16.2% compared to 6.7% nationally. Similarly, the proportion of the workforce in professional and administrative services is 18.6%, compared to 10.6% nationally, and the proportion in information services is 6.2%, compared to 2.2% nationally. Offsetting these sharp divergences on the positive side, there are a far smaller proportion of workers in construction, manufacturing, retail trade, and transportation services. New York County has the greatest skewed income distribution of any county at the upper end, with 23% of families with incomes of over $200,000, compared to 5% nationally. As a result, mean family income is 201% of the national average; median family income is a far more moderate 129% of the average. However, there is also a higher than average proportion of households and families in the two lowest income brackets, with the result that the poverty levels are above average, at 110% of the national average for all families and 107% for all people. The “fat tails” of the income

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distribution at both ends leads to a shortfall in the middle of the distribution, with only 11.2% of the families with income between $50,000 and $75,000 per year, compared to 19.7% nationally. The stark comparison between New York and Bronx counties can be seen by noting that whereas 23% of the families in New York County have incomes of over $200,000, the figure is only 1.5% for the Bronx. The Bronx also has a preponderance of households and families in the lower income brackets, with the result that the poverty rates are more than twice the national average. In terms of occupational distribution, 31.6% of the workforce is employed in education and health care services, compared to 23.2% nationally; the major shortfalls are in construction and manufacturing. Table 7-2. Key Economic Statistics for Kings and Queens Counties Compared to

the U. S. Economy

Kings County,

Queens County,

United States


EMPLOYMENT STATUS

Population 16 years and over 1,984,132 100.0% 1,827,635 100.0% 243,832,923 100.0%

In labor force 1,222,026 61.6% 1,179,450 64.5% 156,966,769 64.4%

Civilian labor force 1,219,822 61.5% 1,178,901 64.5% 155,917,013 63.9%

Employed 1,087,107 54.8% 1,047,992 57.3% 139,033,928 57.0%

Unemployed 132,715 6.7% 130,909 7.2% 16,883,085 6.9%

Armed Forces 2,204 0.1% 549 0.0% 1,049,756 0.4%

OCCUPATION

Civilian employed population 16 + 1,087,107 100.0% 1,047,992 100.0% 139,033,928 100.0%






INDUSTRY

Civilian employed population 16 + 1,087,107 100.0% 1,047,992 100.0% 139,033,928 100.0%

Agriculture and mining 1,294 0.1% 1,469 0.1% 2,646,975 1.9%

Construction 59,330 5.5% 69,941 6.7% 8,686,813 6.2%

Manufacturing 45,713 4.2% 47,429 4.5% 14,439,691 10.4%

Wholesale trade 26,183 2.4% 31,841 3.0% 3,941,066 2.8%

Retail trade 103,875 9.6% 115,464 11.0% 16,203,408 11.7%


Information 41,019 3.8% 27,119 2.6% 3,015,521 2.2%





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INCOME AND BENEFITS


Less than $10,000 112,296 12.4% 56,655 7.3% 8,757,190 7.6%

$10,000 to $14,999 62,769 6.9% 36,674 4.7% 6,668,865 5.8%

$15,000 to $24,999 108,733 12.0% 83,630 10.8% 13,165,380 11.5%

$25,000 to $34,999 98,284 10.9% 78,404 10.2% 12,323,322 10.8%

$35,000 to $49,999 126,417 14.0% 106,169 13.7% 16,312,385 14.2%

$50,000 to $74,999 139,200 15.4% 146,936 19.0% 20,940,859 18.3%

$75,000 to $99,999 88,934 9.8% 96,655 12.5% 13,526,500 11.8%

$100,000 to $149,999 100,257 11.1% 103,831 13.4% 13,544,839 11.8%

$150,000 to $199,999 37,648 4.2% 38,920 5.0% 4,809,998 4.2%

$200,000 or more 30,779 3.4% 24,458 3.2% 4,518,081 3.9%

Median household income (dollars) 42,143 84.2% 53,054 106.0% 50,046

Mean household income (dollars) 62,678 91.8% 68,601 100.5% 68,259

Families 580,453 100.0% 527,729 100.0% 76,089,045 100.0%

Less than $10,000 52,700 9.1% 26,955 5.1% 3,824,251 5.0%

$10,000 to $14,999 34,708 6.0% 18,655 3.5% 2,660,781 3.5%

$15,000 to $24,999 65,194 11.2% 49,143 9.3% 6,770,812 8.9%

$25,000 to $34,999 66,306 11.4% 50,054 9.5% 7,332,318 9.6%

$35,000 to $49,999 86,695 14.9% 71,188 13.5% 10,578,051 13.9%

$50,000 to $74,999 93,018 16.0% 101,812 19.3% 14,990,631 19.7%

$75,000 to $99,999 59,051 10.2% 72,458 13.7% 10,638,931 14.0%

$100,000 to $149,999 71,399 12.3% 84,859 16.1% 11,261,766 14.8%

$150,000 to $199,999 27,929 4.8% 32,510 6.2% 4,130,868 5.4%

$200,000 or more 23,453 4.0% 20,095 3.8% 3,900,636 5.1%

Median family income (dollars) 46,671 77.0% 60,438 99.7% 60,609 Mean family income (dollars) 68,504 86.3% 76,004 95.8% 79,338

Per capita income (dollars) 23,218 89.1% 24,530 94.1% 26,059 PERCENTAGE BELOW POVERTY

All families 19.7% 174.3% 12.1% 107.1% 11.30%

All people 23.0% 150.3% 15.0% 98.0% 15.30%

The occupational distribution in Kings and Queens Counties are similar in many ways except that there are proportionately more employees in health care and information services, which includes newspapers, the Internet, and movie production, in Kings County. It also has a higher proportion in professional services, reflecting the large number of professionals who live “across the bridge” from Manhattan in Brooklyn. The proportions are lower than Queens and the U.S. for manufacturing and wholesale and retail trade; they are lower than Queens but higher than the U.S. average for transportation, entertainment and leisure, and other private services.

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While Kings County has close to the average proportion of individuals and families in the upper income brackets, it has far more than average in the lower brackets. As a result, the poverty rate is well above the national average, at 174% for all families and 150% for all people. The larger proportions at the lower end of the income scale mean that while all broad-based levels of income are below average, the figures are lower for median incomes than for family incomes.

Relative to the U.S., Queens County has a higher than average proportion of the

workforce in transportation, representing the two airports, and also in financial services, entertainment and leisure, and other private services. It has a slightly higher proportion of workers in health care and construction. The major shortfall is in manufacturing, with only 4.5% of the workforce engaged, compared to 10.5% nationally. The proportion of the workforce is also smaller than average in retail trade, professional services, and public administration. In terms of income distribution, Queens is the quintessential “middle class” county, with median and mean family income narrowly straddling the national average; the median family income is 2% higher, while the mean family income is 2% lower. Also, the poverty rate for all families is slightly higher than the national average at 107.1%, while the rate for all people is only 98% of that average. The income distribution is also very similar to the national average.

Table 7-3. Key Economic Statistics for Nassau and Westchester Counties Compared to the U. S. Economy

Nassau County,

Westchester County

United States


EMPLOYMENT STATUS

Population 16 years and over 1,070,507 100.0% 750,071 100.0% 243,832,923 100.0%

In labor force 699,003 65.3% 498,967 66.5% 156,966,769 64.4%


Employed 640,457 59.8% 455,939 60.8% 139,033,928 57.0%

Unemployed 58,172 5.4% 42,959 5.7% 16,883,085 6.9%

Armed Forces 374 0.0% 69 0.0% 1,049,756 0.4%

OCCUPATION







INDUSTRY


Agriculture and mining 1,610 0.3% 671 0.1% 2,646,975 1.9%

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Construction 34,273 5.4% 30,406 6.7% 8,686,813 6.2%

Manufacturing 30,254 4.7% 19,320 4.2% 14,439,691 10.4%

Wholesale trade 24,047 3.8% 11,915 2.6% 3,941,066 2.8%

Retail trade 63,967 10.0% 39,865 8.7% 16,203,408 11.7%


Information 16,473 2.6% 14,139 3.1% 3,015,521 2.2%







INCOME AND BENEFITS


Less than $10,000 13,768 3.1% 15,067 4.4% 8,757,190 7.6%

$10,000 to $14,999 10,351 2.3% 12,484 3.6% 6,668,865 5.8%

$15,000 to $24,999 26,769 6.0% 26,051 7.6% 13,165,380 11.5%

$25,000 to $34,999 25,745 5.8% 23,727 6.9% 12,323,322 10.8%

$35,000 to $49,999 40,845 9.2% 33,119 9.6% 16,312,385 14.2%

$50,000 to $74,999 65,506 14.8% 55,188 16.0% 20,940,859 18.3%

$75,000 to $99,999 55,198 12.5% 41,860 12.2% 13,526,500 11.8%

$100,000 to $149,999 92,813 21.0% 56,073 16.3% 13,544,839 11.8%

$150,000 to $199,999 51,605 11.7% 29,495 8.6% 4,809,998 4.2%

$200,000 or more 60,129 13.6% 51,411 14.9% 4,518,081 3.9%



Families 337,580 100.0% 239,216 100.0% 76,089,045 100.0%

Less than $10,000 6,446 1.9% 5,560 2.3% 3,824,251 5.0%

$10,000 to $14,999 3,784 1.1% 5,732 2.4% 2,660,781 3.5%

$15,000 to $24,999 12,333 3.7% 12,530 5.2% 6,770,812 8.9%

$25,000 to $34,999 14,395 4.3% 12,975 5.4% 7,332,318 9.6%

$35,000 to $49,999 27,236 8.1% 18,894 7.9% 10,578,051 13.9%

$50,000 to $74,999 47,586 14.1% 36,021 15.1% 14,990,631 19.7%

$75,000 to $99,999 43,660 12.9% 29,336 12.3% 10,638,931 14.0%

$100,000 to $149,999 79,586 23.6% 44,959 18.8% 11,261,766 14.8%

$150,000 to $199,999 46,908 13.9% 26,022 10.9% 4,130,868 5.4%

$200,000 or more 55,646 16.5% 47,187 19.7% 3,900,636 5.1%


Mean family income (dollars) 134,223 169.2% 150,577 189.8% 79,338 Per capita income (dollars) 39,935 153.2% 45,554 174.8% 26,059 PERCENTAGE BELOW POVERTY

All families 4.2% 37.2% 6.2% 54.9% 11.30% All people 5.9% 38.6% 8.8% 57.5% 15.30%

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Nassau and Westchester counties are similar in terms of occupational distribution and income levels, although Nassau County has a higher median income, while Westchester has a higher mean income. Both counties have approximately the same mix of employment, although Westchester County has proportionately more workers in the professional services industry and construction, but fewer in and manufacturing. Both counties have proportionately more workers in education and health care services, and fewer workers in entertainment and leisure. In terms of income distribution, both counties have very few households and families at the lower end of the income scale, so the poverty rate in these two counties is only 37.2% for all families in Nassau and 54.9% in Westchester. At the top end of the income scale, 16.6% of all families in Nassau County had incomes of over $200,000, while the figure was 20.0% for Westchester County. As a result, median household and family income in Nassau is above Westchester, while the reverse is true for mean income.

Table 7-4. Key Economic Statistics for Richmond and Suffolk Counties

Compared to the U. S. Economy

Category Richmond % Suffolk % U.S. %

EMPLOYMENT STATUS

Population 16 years and over 374,998 100.0% 1,183,272 100.0% 243,832,923 100.0%

In labor force 219,951 58.7% 786,949 66.5% 156,966,769 64.4%


Employed 199,327 53.2% 720,726 60.9% 139,033,928 57.0%

Unemployed 19,938 5.3% 65,712 5.6% 16,883,085 6.9%

Armed Forces 686 0.2% 511 0.0% 1,049,756 0.4%

Not in labor force 155,047 41.3% 396,323 33.5% 86,866,154 35.6%

OCCUPATION

Civilian employed population 16+ 199,327 100.0% 720,726 100.0% 139,033,928 100.0%

Management & professional 78,746 39.5% 269,961 37.5% 49,975,620 35.9%



Construction, maintenance, repair 18,218 9.1% 67,224 9.3% 12,697,304 9.1%

Production & transportation 17,243 8.7% 73,340 10.2% 16,590,396 11.9%

INDUSTRY

Civilian employed population 16+ 199,327 100.0% 720,726 100.0% 139,033,928 100.0%

Agriculture & mining 198 0.1% 2,164 0.3% 2,646,975 1.9%

Construction 14,859 7.5% 56,469 7.8% 8,686,813 6.2%

Manufacturing 6,854 3.4% 55,922 7.8% 14,439,691 10.4%

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Wholesale trade 2,885 1.4% 25,800 3.6% 3,941,066 2.8%

Retail trade 20,115 10.1% 87,305 12.1% 16,203,408 11.7%

Transportation & utilities 13,274 6.7% 40,414 5.6% 6,843,579 4.9%

Information 5,690 2.9% 20,802 2.9% 3,015,521 2.2%

Finance, insurance, & real estate 21,756 10.9% 51,642 7.2% 9,275,465 6.7%

Professional & administrative 19,756 9.9% 77,995 10.8% 14,710,089 10.6%

Educational services & health care 57,897 29.0% 183,819 25.5% 32,311,107 23.2%

Arts, entertain, hotel, food svcs 11,295 5.7% 46,082 6.4% 12,859,572 9.2%

Other private services 9,892 5.0% 31,567 4.4% 6,913,449 5.0%


INCOME AND BENEFITS


Less than $10,000 11,750 7.2% 16,720 3.4% 8,757,190 7.6%

$10,000 to $14,999 6,443 3.9% 12,963 2.6% 6,668,865 5.8%

$15,000 to $24,999 14,588 8.9% 33,358 6.7% 13,165,380 11.5%

$25,000 to $34,999 12,204 7.4% 29,366 5.9% 12,323,322 10.8%

$35,000 to $49,999 15,796 9.6% 50,072 10.1% 16,312,385 14.2%

$50,000 to $74,999 24,979 15.2% 83,600 16.8% 20,940,859 18.3%

$75,000 to $99,999 21,057 12.9% 74,812 15.1% 13,526,500 11.8%

$100,000 to $149,999 31,930 19.5% 102,723 20.7% 13,544,839 11.8%

$150,000 to $199,999 14,951 9.1% 48,792 9.8% 4,809,998 4.2%

$200,000 or more 10,118 6.2% 43,860 8.8% 4,518,081 3.9%



Families 124,237 100.0% 372,321 100.0% 76,089,045 100.0%

Less than $10,000 6,797 5.5% 6,585 1.8% 3,824,251 5.0%

$10,000 to $14,999 2,444 2.0% 4,653 1.2% 2,660,781 3.5%

$15,000 to $24,999 7,235 5.8% 14,957 4.0% 6,770,812 8.9%

$25,000 to $34,999 8,334 6.7% 20,205 5.4% 7,332,318 9.6%

$35,000 to $49,999 11,322 9.1% 34,188 9.2% 10,578,051 13.9%

$50,000 to $74,999 19,808 15.9% 62,596 16.8% 14,990,631 19.7%

$75,000 to $99,999 16,461 13.2% 57,797 15.5% 10,638,931 14.0%

$100,000 to $149,999 28,452 22.9% 88,657 23.8% 11,261,766 14.8%

$150,000 to $199,999 13,700 11.0% 43,244 11.6% 4,130,868 5.4%

$200,000 or more 9,684 7.8% 39,439 10.6% 3,900,636 5.1%


Mean family income (dollars) 97,598 123.0% 113,810 143.4% 79,338

Per capita income (dollars) 30,122 115.6% 34,582 132.7% 26,059

Median earnings for workers 42,308 146.4% 39,457 136.5% 28,899

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Median earnings for male full-time 61,306 131.8% 62,200 133.8% 46,500

Median earnings for female full-time 51,107 139.8% 45,975 125.8% 36,551

PERCENTAGE BELOW POVERTY LEVEL

All families 9.60% 85.0% 4.10% 36.3% 11.30%

All people 11.80% 77.1% 6.20% 40.5% 15.30%

Richmond County (Staten Island) has a disproportionately high share of its

workforce in health care, finance, and public administration; this is offset by lower than average shares in manufacturing and leisure. Median household and family income levels are 35-40% above the comparable national figures; mean income levels are about 25% higher. This dichotomy is driven by the presence of families earning less than $10,000 per year – 5.5% in Richmond County, as compared to 5% for the nation. Along the same lines, 10% of families in the county live in poverty – very similar to the national average (11%).

Residents of Suffolk County are not quite as affluent as their neighbors in Nassau County, although mean and median income levels are still about 40-60% higher than the national figures. The distribution of the workforce is quite similar to the national averages, with deviations only in construction (8% vs. 6% for the U.S.), manufacturing (8% vs. 10%), and leisure (6% vs. 9%). Table 7-5. Key Economic Statistics for Rockland County, Compared to the U. S.

Economy

Rockland

United States

Category Estimate Percent Estimate Percent EMPLOYMENT STATUS Population 16 years and over 237,613 100.0% 246,194,111 100.0%

In labor force 158,011 66.5% 157,476,287 64.0% Civilian labor force 157,800 66.4% 156,460,172 63.6% Employed 142,671 60.0% 140,399,548 57.0% Unemployed 15,129 6.4% 16,060,624 6.5% Armed Forces 211 0.1% 1,016,115 0.4% Not in labor force 79,602 33.5% 88,717,824 36.0%

OCCUPATION Civilian employed population 16+ 142,671 100.0% 140,399,548 100.0% Management & professional 62,976 44.1% 50,508,936 36.0% Service occupations 23,554 16.5% 25,739,562 18.3% Sales and office occupations 33,713 23.6% 34,447,704 24.5% Construction, maintenance, repair 12,480 8.7% 12,748,750 9.1% Production & transportation 9,948 7.0% 16,954,596 12.1%

30

INDUSTRY Civilian employed population 16+ 142,671 100.0% 140,399,548 100.0% Agriculture & mining 582 0.4% 2,720,289 1.9% Construction 8,925 6.3% 8,563,737 6.1% Manufacturing 9,963 7.0% 14,665,712 10.4% Wholesale trade 4,174 2.9% 3,894,622 2.8% Retail trade 15,385 10.8% 16,335,831 11.6% Transportation & utilities 6,880 4.8% 6,987,923 5.0% Information 4,908 3.4% 2,950,890 2.1% Finance, insurance, & real estate 9,228 6.5% 9,233,893 6.6%

Professional & administrative 14,606 10.2% 15,079,731 10.7% Educational services & health care 44,756 31.4% 32,601,321 23.2%

Arts, entertain, hotel, food svcs 10,519 7.4% 13,210,187 9.4% Other private services 7,084 5.0% 7,056,697 5.0% Public administration 5,661 4.0% 7,098,715 5.1% INCOME AND BENEFITS Total households 98,214 100.0% 114,991,725 100.0% Less than $10,000 3,693 3.8% 9,004,208 7.8% $10,000 to $14,999 3,284 3.3% 6,678,477 5.8% $15,000 to $24,999 7,667 7.8% 13,137,384 11.4% $25,000 to $34,999 5,045 5.1% 12,153,825 10.6%

$35,000 to $49,999 9,775 10.0% 15,933,833 13.9% $50,000 to $74,999 15,028 15.3% 20,697,584 18.0% $75,000 to $99,999 14,112 14.4% 13,503,035 11.7% $100,000 to $149,999 18,281 18.6% 13,864,597 12.1% $150,000 to $199,999 11,029 11.2% 5,110,706 4.4% $200,000 or more 10,300 10.5% 4,908,076 4.3% Median household income (dollars) 82,217 162.8% 50,502 Mean household income (dollars) 104,111 149.1% 69,821 Families 77,030 100.0% 76,084,006 100.0% Less than $10,000 2,226 2.9% 3,871,317 5.1% $10,000 to $14,999 1,908 2.5% 2,657,221 3.5%

$15,000 to $24,999 5,171 6.7% 6,713,707 8.8% $25,000 to $34,999 3,329 4.3% 7,182,203 9.4% $35,000 to $49,999 6,335 8.2% 10,267,725 13.5% $50,000 to $74,999 11,820 15.3% 14,712,193 19.3% $75,000 to $99,999 10,983 14.3% 10,561,271 13.9% $100,000 to $149,999 15,702 20.4% 11,510,540 15.1% $150,000 to $199,999 10,032 13.0% 4,373,589 5.7%

31

$200,000 or more 9,524 12.4% 4,234,240 5.6% Median family income (dollars) 91,254 148.5% 61,455 Mean family income (dollars) 113,546 139.5% 81,375 Per capita income (dollars) 33,209 124.3% 26,708 Median earnings for workers 38,087 128.9% 29,538 Median earnings for male full-time 61,877 131.7% 46,993 Median earnings for female full-time 53,274 143.5% 37,133 PERCENTAGE BELOW POVERTY LEVEL All families 9.80% 83.8% 11.70% All people 14.10% 88.7% 15.90%

Rockland County is to the west of Westchester, and while the section of Rockland County that is closest to Westchester – and the Hudson River – maintains the same upscale characteristics, the western part of the county has a much lower level of income and higher incidence of poverty. Hence we find that mean family income is 140% of the national average, compared to over 180% in Westchester, and the poverty rate for all families is 84%, compared to 64%.

In terms of employment distribution, the proportion of workers in most sectors is close to the national average except for education and health care services, which employ 31.4% of the workforce, compared to 23.2% nationally. Offsetting that, the proportions in other industry sectors except construction are all slightly lower, with the biggest decrement occurring in manufacturing.

Table 7-6. Labor Market Statistics, 2004-2013, for 9 New York Counties in the New

York City Metropolitan Area

Labor Force Employed Unemployed Un Rate, %

9 counties 2004 5,818,916 5,460,625 358,291 6.2

2005 5,859,826 5,555,987 303,839 5.2

2006 5,938,726 5,666,010 272,716 4.6

2007 5,991,709 5,719,898 271,811 4.5

2008 6,056,345 5,738,289 318,056 5.3

2009 6,088,313 5,569,700 518,613 8.5

2010 6,069,507 5,535,200 534,307 8.8

2011 6,059,379 5,551,890 507,489 8.4

2012 6,138,808 5,609,988 528,820 8.6

2013 6,176,729 5,694,168 482,561 7.8

32

Bronx 2004 500,772 454,693 46,079 9.2

2005 501,171 463,343 37,828 7.5

2006 505,874 472,201 33,673 6.7

2007 512,950 478,688 34,262 6.7

2008 521,475 483,104 38,371 7.4

2009 534,756 470,986 63,770 11.9

2010 546,697 476,757 69,940 12.8

2011 545,135 477,354 67,781 12.4

2012 554,496 483,628 70,868 12.8

2013 557,683 491,658 66,025 11.8

Kings 2004 1,054,993 974,474 80,519 7.6

2005 1,057,799 991,983 65,816 6.2

2006 1,074,796 1,017,222 57,574 5.4

2007 1,087,771 1,029,883 57,888 5.3

2008 1,101,944 1,037,345 64,599 5.9

2009 1,121,941 1,011,563 110,378 9.8

2010 1,123,502 1,007,856 115,646 10.3

2011 1,125,690 1,015,322 110,368 9.8

2012 1,144,536 1,029,878 114,658 10.0

2013 1,155,342 1,046,979 108,363 9.4

New York 2004 881,645 827,086 54,559 6.2

2005 892,020 847,100 44,920 5.0

2006 909,985 870,764 39,221 4.3

2007 918,941 879,937 39,004 4.2

2008 928,288 883,946 44,342 4.8

2009 933,497 855,436 78,061 8.4

2010 925,233 850,379 74,854 8.1

2011 925,295 855,815 69,480 7.5

2012 939,618 866,092 73,526 7.8

2013 949,131 880,473 68,658 7.2

Queens 2004 1,069,811 1,002,154 67,657 6.3

2005 1,077,108 1,020,778 56,330 5.2

2006 1,092,609 1,043,607 49,002 4.5

2007 1,103,964 1,055,609 48,355 4.4

33

2008 1,116,452 1,061,882 54,570 4.9

2009 1,131,695 1,037,420 94,275 8.3

2010 1,122,105 1,024,468 97,637 8.7

2011 1,119,930 1,028,506 91,424 8.2

2012 1,136,610 1,041,256 95,354 8.4

2013 1,147,536 1,058,546 88,990 7.8

Nassau 2004 686,062 655,126 30,936 4.5

2005 690,300 662,102 28,198 4.1

2006 694,908 668,323 26,585 3.8

2007 695,439 669,390 26,049 3.7

2008 698,151 665,075 33,076 4.7

2009 690,790 642,498 48,292 7.0

2010 689,443 640,581 48,862 7.1

2011 686,611 640,137 46,474 6.8

2012 695,308 646,692 48,616 7.0

2013 697,676 656,242 41,434 5.9

Westchester 2004 479,130 457,779 21,351 4.5

2005 482,934 463,358 19,576 4.1

2006 486,334 467,439 18,895 3.9

2007 489,955 471,469 18,486 3.8

2008 494,036 470,369 23,667 4.8

2009 486,164 451,269 34,895 7.2

2010 476,837 441,886 34,951 7.3

2011 474,741 441,530 33,211 7.0

2012 477,250 442,960 34,290 7.2

2013 475,390 445,482 29,908 6.3

Suffolk 2004 770,822 734,782 36,040 4.7

2005 778,417 745,348 33,069 4.2

2006 785,328 753,921 31,407 4.0

2007 788,560 757,585 30,975 3.9

2008 797,099 757,128 39,971 5.0

2009 789,494 731,309 58,185 7.4

2010 786,668 726,643 60,025 7.6

2011 784,404 726,138 58,266 7.4

2012 790,988 731,190 59,798 7.6

2013 792,842 741,988 50,854 6.4

34

Richmond 2004 225,510 211,038 14,472 6.4

2005 228,713 216,711 12,002 5.2

2006 236,857 226,293 10,564 4.5

2007 240,136 229,384 10,752 4.5

2008 243,269 231,214 12,055 5.0

2009 246,594 226,538 20,056 8.1

2010 241,816 220,511 21,305 8.8

2011 240,526 220,507 20,019 8.3

2012 241,833 220,914 20,919 8.7

2013 243,573 224,583 18,990 7.8

Rockland 2004 150,171 143,493 6,678 4.4

2005 151,364 145,264 6,100 4.0

2006 152,035 146,240 5,795 3.8

2007 153,993 147,953 6,040 3.9

2008 155,631 148,226 7,405 4.8

2009 153,382 142,681 10,701 7.0

2010 157,206 146,119 11,087 7.1

2011 157,047 146,581 10,466 6.7

2012 158,169 147,378 10,791 6.8

2013 157,556 148,217 9,339 5.9

In spite of the collapse of the financial sector in late 2008, this area was not particularly hard hit by the recession. The unemployment rate for the 9-county region rose to 8.5% in 2009 and 8.8% in 2010, but these figures were below the 9.3% and 9.6% figures for the overall U. S. economy. The decline to 8.4% in 2011 was generally in line with, but significantly less than, the decline in the national rate from 9.6% to 8.9%. However, there was a surprising upturn in 2012, with an average increase of 0.2% in all the 9-county region. Except for Rockland County, where the rate edged up only 0.1%, the unemployment rate rose between 0.2% and 0.4% in each county. This appears to have been a one-time fluke, as the unemployment rate fell sharply from 8.6% to 7.8% for the 9-county region in 2013. Here again, all 9 counties followed suit, with declines ranging from 0.6% to 1.2% in the individual counties.

35

Table 7-7. Level and Growth of Population, State of New York, and 9 New York Counties in the New York City Metropolitan Area

Note: for easier viewing, each of these next two tables is divided into two parts

NY State Man- Bronx Kings Queens

hattan

2011 19,465,197 1,601,948 1,392,002 2,532,645 2,247,848

2010 19,395,206 1,587,481 1,387,159 2,508,515 2,233,895

2009 19,307,066 1,583,431 1,376,261 2,487,751 2,217,166

2008 19,212,436 1,587,022 1,363,488 2,460,361 2,193,623

2007 19,132,335 1,581,402 1,354,056 2,441,324 2,177,351

2006 19,104,631 1,578,171 1,348,164 2,436,132 2,173,862

2005 19,132,610 1,573,573 1,351,736 2,445,809 2,185,222

2004 19,171,567 1,569,947 1,358,963 2,459,094 2,198,516

2003 19,175,939 1,562,154 1,362,373 2,472,999 2,214,608

2002 19,137,800 1,555,382 1,358,739 2,480,559 2,224,507

2011/10 0.36% 0.91% 0.35% 0.96% 0.62%

2010/09 0.46% 0.26% 0.79% 0.83% 0.75%

2009/08 0.49% -0.23% 0.94% 1.11% 1.07%

2008/07 0.42% 0.36% 0.70% 0.78% 0.75%

2007/06 0.15% 0.20% 0.44% 0.21% 0.16%

2006/05 -0.15% 0.29% -0.26% -0.40% -0.52%

2005/04 -0.20% 0.23% -0.53% -0.54% -0.60%

2004/03 -0.02% 0.50% -0.25% -0.56% -0.73%

2003/02 0.20% 0.44% 0.27% -0.30% -0.44%

2011/02 0.19% 0.33% 0.27% 0.23% 0.12%

West- Nassau Richmond Rockland Suffolk 9 counties

chester

2011 955,899 1,344,436 470,467 315,158 1,498,816 12,359,219

2010 950,283 1,341,033 469,393 312,520 1,494,388 12,284,667

2009 944,201 1,332,088 466,965 308,652 1,487,206 12,203,721

2008 937,449 1,325,129 463,701 305,413 1,480,218 12,116,404

2007 933,414 1,322,048 459,642 301,668 1,475,255 12,046,160

2006 931,426 1,324,905 457,577 299,390 1,475,626 12,025,253

2005 933,401 1,332,318 457,028 298,737 1,477,687 12,055,511

2004 935,457 1,337,964 456,846 297,562 1,478,215 12,092,564

2003 935,799 1,339,761 455,939 296,224 1,470,849 12,110,706

2002 935,219 1,339,572 452,813 293,728 1,456,745 12,097,264

36

2011/10 0.59% 0.25% 0.23% 0.84% 0.30% 0.61%

2010/09 0.64% 0.67% 0.52% 1.25% 0.48% 0.66%

2009/08 0.72% 0.53% 0.70% 1.06% 0.47% 0.72%

2008/07 0.43% 0.23% 0.88% 1.24% 0.34% 0.58%

2007/06 0.21% -0.22% 0.45% 0.76% -0.03% 0.17%

2006/05 -0.21% -0.56% 0.12% 0.22% -0.14% -0.25%

2005/04 -0.22% -0.42% 0.04% 0.39% -0.04% -0.31%

2004/03 -0.04% -0.13% 0.20% 0.45% 0.50% -0.15%

2003/02 0.06% 0.01% 0.69% 0.85% 0.97% 0.11%

2011/02 0.24% 0.04% 0.43% 0.78% 0.32% 0.24%

Population growth in this 9-county area was well below the 1% rate for the U.S.; although it is slightly higher than for the entire state. In recent years, population growth in Queens has been higher than any of the other counties in this region; it has been lowest for Nassau County. The growth in Manhattan, which was the only county to turn negative in 2009, rebounded sharply in 2011. Growth in Queens is due largely to the increased number of immigrants, both entering the area for the first time and moving out of substandard housing in Manhattan.

Population growth for the area actually declined from 2003 through 2006, as an increasing proportion of the population chose to move to warmer and sunnier climates; but when the housing collapse temporarily halted the southern exposure, population growth picked up. With the pickup in housing markets generally, population growth slowed again; but it remains positive, and a return to the decline of the previous decade seems highly unlikely for the next few years.

37

Table 7-8. Level and Growth of Personal Income (Billion $), State of New York and 9 New York Counties in the New York City Metropolitan Area

NY State Man- Bronx Kings Queens

hattan

2011 995.18 194.32 44.25 99.66 94.45

2010 952.67 183.66 42.60 95.05 89.94

2009 902.38 165.78 40.33 89.50 86.10

2008 949.25 187.73 39.72 89.94 88.31

2007 915.53 187.82 38.30 84.61 84.68

2006 851.44 176.92 35.27 77.71 77.37

2005 786.51 155.77 33.03 72.23 72.38

2004 741.17 137.78 32.28 70.05 70.15

2003 695.39 127.64 30.32 65.11 66.27

2002 678.39 125.99 29.32 63.72 64.56

2011/10 4.46% 5.80% 3.88% 4.85% 5.02%

2010/09 5.57% 10.78% 5.62% 6.20% 4.45%

2009/08 -4.94% -11.69% 1.53% -0.49% -2.50%

2008/07 3.68% -0.05% 3.72% 6.30% 4.29%

2007/06 7.53% 6.16% 8.58% 8.87% 9.45%

2006/05 8.25% 13.57% 6.77% 7.59% 6.89%

2005/04 6.12% 13.06% 2.34% 3.12% 3.18%

2004/03 6.58% 7.95% 6.44% 7.58% 5.86%

2003/02 2.51% 1.31% 3.42% 2.18% 2.64%

2011/02 4.35% 4.93% 4.67% 5.09% 4.31%

West- Nassau Richmond Rockland Suffolk 9 counties

chester

2011 72.51 91.12 22.81 16.95 78.46 714.53

2010 69.85 88.06 22.05 16.11 75.78 683.10

2009 67.25 85.24 21.48 15.60 72.73 644.01

2008 72.88 91.33 22.22 16.45 77.10 685.68

2007 72.03 87.17 21.14 16.12 74.88 666.75

2006 66.65 80.88 19.43 14.85 68.63 617.71

2005 60.49 75.46 18.17 13.75 63.57 564.85

2004 56.30 70.40 17.36 13.20 59.91 527.43

2003 51.89 67.17 16.39 12.18 55.55 492.52

2002 51.02 65.67 15.94 11.97 54.16 482.35

38

2011/10 3.81% 3.48% 3.44% 5.23% 3.54% 4.60%

2010/09 3.86% 3.31% 2.67% 3.28% 4.20% 6.07%

2009/08 -7.72% -6.67% -3.31% -5.18% -5.67% -6.08%

2008/07 1.18% 4.77% 5.10% 2.02% 2.96% 2.84%

2007/06 8.06% 7.78% 8.81% 8.60% 9.10% 7.94%

2006/05 10.19% 7.18% 6.92% 8.00% 7.95% 9.36%

2005/04 7.44% 7.19% 4.67% 4.17% 6.11% 7.09%

2004/03 8.49% 4.81% 5.90% 8.32% 7.85% 7.09%

2003/02 1.71% 2.29% 2.83% 1.80% 2.56% 2.11%

2011/02 3.98% 3.70% 4.06% 3.94% 4.20% 4.46%

The growth in personal income over the decade has been almost the same for this 9-county region as for the State of New York; with both figures well above the 3.7% average growth for the U.S. economy. The swings in New York County are by far the largest, reflecting the boom and bust periods of financial markets; but Nassau and Westchester were also hard hit in 2009, whereas the decline in income in Queens was much more modest, and income in the Bronx actually increased in 2009, as few of those residents worked in financial markets in any case. The entire area rebounded sharply in the past two years as the stock market returned to its previous levels.

39

Group B. Bergen, Essex, Hudson, Middlesex, Morris, Monmouth, and Union Counties in New Jersey

Table 7-9. Economic Profile of Essex and Hudson Counties, NJ and

Comparison with the U.S Data

Category Essex % Hudson % U.S. % EMPLOYMENT STATUS Population 16 years and over 612,565 100.0% 520,559 100.0% 243,832,923 100.0% In labor force 400,770 65.4% 359,487 69.1% 156,966,769 64.4% Civilian labor force 400,523 65.4% 359,408 69.0% 155,917,013 63.9% Employed 344,146 56.2% 312,480 60.0% 139,033,928 57.0% Unemployed 56,377 9.2% 46,928 9.0% 16,883,085 6.9% Armed Forces 247 0.0% 79 0.0% 1,049,756 0.4% Not in labor force 211,795 34.6% 161,072 30.9% 86,866,154 35.6% OCCUPATION Civilian employed population 16+ 344,146 100.0% 312,480 100.0% 139,033,928 100.0% Management & professional 128,336 37.3% 118,514 37.9% 49,975,620 35.9% Service occupations 70,110 20.4% 54,500 17.4% 25,059,153 18.0% Sales and office occupations 83,284 24.2% 75,993 24.3% 34,711,455 25.0% Construction, maintenance, repair 24,850 7.2% 22,231 7.1% 12,697,304 9.1% Production & transportation 37,566 10.9% 41,242 13.2% 16,590,396 11.9% INDUSTRY Civilian employed population 16+ 344,146 100.0% 312,480 100.0% 139,033,928 100.0% Agriculture & mining 839 0.2% 88 0.0% 2,646,975 1.9% Construction 19,412 5.6% 17,452 5.6% 8,686,813 6.2% Manufacturing 21,063 6.1% 25,036 8.0% 14,439,691 10.4% Wholesale trade 8,192 2.4% 12,919 4.1% 3,941,066 2.8% Retail trade 33,180 9.6% 31,641 10.1% 16,203,408 11.7% Transportation & utilities 24,477 7.1% 24,887 8.0% 6,843,579 4.9% Information 11,875 3.5% 10,909 3.5% 3,015,521 2.2% Finance, insurance, & real estate 31,756 9.2% 34,463 11.0% 9,275,465 6.7% Professional & administrative 44,064 12.8% 42,737 13.7% 14,710,089 10.6% Educational services & health care 89,318 26.0% 60,295 19.3% 32,311,107 23.2% Arts, entertain, hotel, food svcs 25,779 7.5% 23,187 7.4% 12,859,572 9.2% Other private services 17,380 5.1% 16,461 5.3% 6,913,449 5.0% Public administration 16,811 4.9% 12,405 4.0% 7,187,193 5.2% INCOME AND BENEFITS Total households 275,417 100.0% 238,692 100.0% 114,567,419 100.0% Less than $10,000 28,243 10.3% 19,411 8.1% 8,757,190 7.6% $10,000 to $14,999 16,478 6.0% 14,462 6.1% 6,668,865 5.8% $15,000 to $24,999 28,288 10.3% 24,152 10.1% 13,165,380 11.5% $25,000 to $34,999 25,719 9.3% 20,533 8.6% 12,323,322 10.8% $35,000 to $49,999 32,635 11.8% 29,768 12.5% 16,312,385 14.2%

40

$50,000 to $74,999 44,176 16.0% 44,776 18.8% 20,940,859 18.3% $75,000 to $99,999 31,262 11.4% 26,814 11.2% 13,526,500 11.8% $100,000 to $149,999 32,280 11.7% 31,304 13.1% 13,544,839 11.8% $150,000 to $199,999 14,778 5.4% 11,757 4.9% 4,809,998 4.2% $200,000 or more 21,558 7.8% 15,715 6.6% 4,518,081 3.9% Median household income (dollars) 52,394 104.7% 54,817 109.5% 50,046 Mean household income (dollars) 80,167 117.4% 76,339 111.8% 68,259 Families 175,731 100.0% 147,709 100.0% 76,089,045 100.0% Less than $10,000 12,211 6.9% 8,382 5.7% 3,824,251 5.0% $10,000 to $14,999 6,627 3.8% 7,409 5.0% 2,660,781 3.5% $15,000 to $24,999 15,282 8.7% 14,311 9.7% 6,770,812 8.9% $25,000 to $34,999 15,561 8.9% 12,795 8.7% 7,332,318 9.6% $35,000 to $49,999 19,250 11.0% 20,209 13.7% 10,578,051 13.9% $50,000 to $74,999 26,811 15.3% 27,360 18.5% 14,990,631 19.7% $75,000 to $99,999 21,661 12.3% 16,708 11.3% 10,638,931 14.0% $100,000 to $149,999 25,935 14.8% 21,969 14.9% 11,261,766 14.8% $150,000 to $199,999 13,439 7.6% 8,032 5.4% 4,130,868 5.4% $200,000 or more 18,954 10.8% 10,534 7.1% 3,900,636 5.1% Median family income (dollars) 66,439 109.6% 57,978 95.7% 60,609 Mean family income (dollars) 97,237 122.6% 81,559 102.8% 79,338 Per capita income (dollars) 29,674 113.9% 29,798 114.3% 26,059 Median earnings for workers 32,961 114.1% 35,677 123.5% 28,899 Median earnings for male full-time 49,597 106.7% 50,563 108.7% 46,500 Median earnings for female full-time 41,317 113.0% 41,173 112.6% 36,551 PERCENTAGE BELOW POVERTY LEVEL All families 13.9% 123.0% 13.7% 121.2% 11.3%

All people 16.7% 109.2% 16.5% 107.8% 15.3%

The income distributions in Essex and Hudson Counties can best be described as “fat-tailed”, with greater than average percentages in the highest and lowest income brackets. To elaborate, 11% of families in each of the two counties earn less than $15,000 a year, compared to 8% nationally – while 11% of Essex families and 7% of Hudson families earn $200,000 or more, compared to 5% for the U.S. This dichotomy can also be seen in the high mean household incomes ($80K in Essex and $76K in Hudson, versus $68K for the U.S.) and large share of families living in poverty (14% in each county, versus 11% for the nation). Turning to the occupation data, both counties have lower than average shares in manufacturing as well as the arts, entertainment, hotel, and food service industries – and higher than average shares in transportation and finance. The counties differ in the mix of workers in the education and health care industries, as Essex (26%) has a higher proportion than average and Hudson has a lower proportion at 19%.

41

Table 7-10. Economic Profile of Bergen and Morris Counties, NJ and Comparison with the U.S Data

Category Bergen % Morris % U.S. % EMPLOYMENT STATUS Population 16 years and over 727,196 100.0% 389,318 100.0% 243,832,923 100.0% In labor force 478,944 65.9% 265,835 68.3% 156,966,769 64.4% Civilian labor force 478,892 65.9% 265,835 68.3% 155,917,013 63.9% Employed 438,302 60.3% 242,762 62.4% 139,033,928 57.0% Unemployed 40,590 5.6% 23,073 5.9% 16,883,085 6.9% Armed Forces 52 0.0% 0 0.0% 1,049,756 0.4% Not in labor force 248,252 34.1% 123,483 31.7% 86,866,154 35.6% OCCUPATION Civilian employed population 16+ 438,302 100.0% 242,762 100.0% 139,033,928 100.0% Management & professional 201,513 46.0% 117,011 48.2% 49,975,620 35.9% Service occupations 55,159 12.6% 31,488 13.0% 25,059,153 18.0% Sales and office occupations 114,453 26.1% 61,530 25.3% 34,711,455 25.0% Construction, maintenance, repair 28,908 6.6% 13,971 5.8% 12,697,304 9.1% Production & transportation 38,269 8.7% 18,762 7.7% 16,590,396 11.9% INDUSTRY Civilian employed population 16+ 438,302 100.0% 242,762 100.0% 139,033,928 100.0% Agriculture & mining 919 0.2% 605 0.2% 2,646,975 1.9% Construction 24,897 5.7% 13,025 5.4% 8,686,813 6.2% Manufacturing 40,015 9.1% 29,462 12.1% 14,439,691 10.4% Wholesale trade 19,216 4.4% 8,531 3.5% 3,941,066 2.8% Retail trade 47,458 10.8% 24,489 10.1% 16,203,408 11.7% Transportation & utilities 22,703 5.2% 11,615 4.8% 6,843,579 4.9% Information 16,169 3.7% 10,352 4.3% 3,015,521 2.2% Finance, insurance, & real estate 45,159 10.3% 26,164 10.8% 9,275,465 6.7% Professional & administrative 58,730 13.4% 33,295 13.7% 14,710,089 10.6% Educational services & health care 99,084 22.6% 55,177 22.7% 32,311,107 23.2% Arts, entertain, hotel, food svcs 28,699 6.5% 12,728 5.2% 12,859,572 9.2% Other private services 20,540 4.7% 8,589 3.5% 6,913,449 5.0% Public administration 14,713 3.4% 8,730 3.6% 7,187,193 5.2% INCOME AND BENEFITS Total households 333,002 100.0% 177,786 100.0% 114,567,419 100.0% Less than $10,000 15,136 4.5% 5,141 2.9% 8,757,190 7.6% $10,000 to $14,999 12,370 3.7% 3,562 2.0% 6,668,865 5.8% $15,000 to $24,999 24,587 7.4% 10,598 6.0% 13,165,380 11.5% $25,000 to $34,999 23,753 7.1% 10,446 5.9% 12,323,322 10.8% $35,000 to $49,999 33,430 10.0% 15,265 8.6% 16,312,385 14.2% $50,000 to $74,999 53,157 16.0% 27,277 15.3% 20,940,859 18.3% $75,000 to $99,999 40,999 12.3% 25,266 14.2% 13,526,500 11.8% $100,000 to $149,999 56,634 17.0% 33,587 18.9% 13,544,839 11.8%

42

$150,000 to $199,999 34,456 10.3% 20,542 11.6% 4,809,998 4.2% $200,000 or more 38,480 11.6% 26,102 14.7% 4,518,081 3.9% Median household income (dollars) 77,389 154.6% 91,469 182.8% 50,046 Mean household income (dollars) 105,488 154.5% 121,784 178.4% 68,259 Families 236,574 100.0% 128,754 100.0% 76,089,045 100.0% Less than $10,000 6,237 2.6% 1,983 1.5% 3,824,251 5.0% $10,000 to $14,999 4,959 2.1% 1,149 0.9% 2,660,781 3.5% $15,000 to $24,999 11,365 4.8% 4,287 3.3% 6,770,812 8.9% $25,000 to $34,999 12,914 5.5% 5,257 4.1% 7,332,318 9.6% $35,000 to $49,999 19,255 8.1% 9,063 7.0% 10,578,051 13.9% $50,000 to $74,999 36,079 15.3% 18,910 14.7% 14,990,631 19.7% $75,000 to $99,999 29,860 12.6% 18,470 14.3% 10,638,931 14.0% $100,000 to $149,999 49,242 20.8% 27,700 21.5% 11,261,766 14.8% $150,000 to $199,999 31,650 13.4% 18,007 14.0% 4,130,868 5.4% $200,000 or more 35,013 14.8% 23,928 18.6% 3,900,636 5.1% Median family income (dollars) 97,394 160.7% 107,639 177.6% 60,609 Mean family income (dollars) 123,384 155.5% 141,174 177.9% 79,338 Per capita income (dollars) 39,409 151.2% 44,393 170.4% 26,059 Median earnings for workers 44,350 153.5% 48,157 166.6% 28,899 Median earnings for male full-time 63,074 135.6% 77,163 165.9% 46,500 Median earnings for female full-time 51,103 139.8% 55,422 151.6% 36,551 PERCENTAGE BELOW POVERTY LEVEL All families 5.6% 49.6% 3.7% 32.7% 11.3% All people 6.8% 44.4% 6.0% 39.2% 15.3%

Bergen and Morris counties are two of the more affluent counties in New Jersey; household and family income levels are 50-60% above the national figures in Bergen County and 75-85% above the national figures in Morris County. Along the same lines, the poverty rates in both counties are quite low, at less than 50% of the national rate.

Consistent with its high-income profile, nearly half of the workforce is in management and professional jobs – 46% in Bergen and 48% in Morris, well above the national percentage (36%). On the other side of the coin, each county has a smaller mix of workers in service and production & transportation as compared to the rest of the nation. Table 7-11. Economic Profile of Middlesex, Monmouth, and Union Counties, NJ,

and Comparison with the U.S Data

Category Middlesex Monmouth Union

Population 16 years and over 661,385 100.0% 504,018 100.0% 427,489 100.0%

In labor force 433,491 65.5% 335,352 66.5% 295,379 69.1%

43

Civilian labor force 433,329 65.5% 334,970 66.5% 295,280 69.1%

Employed 397,038 60.0% 305,207 60.6% 261,426 61.2%

Unemployed 36,291 5.5% 29,763 5.9% 33,854 7.9%

Armed Forces 162 0.0% 382 0.1% 99 0.0%

Not in labor force 227,894 34.5% 168,666 33.5% 132,110 30.9%

OCCUPATION

Civilian employed population 16+ 397,038 100.0% 305,207 100.0% 261,426 100.0%

Management & professional 173,229 43.6% 128,209 42.0% 90,270 34.5%

Service occupations 55,695 14.0% 49,434 16.2% 43,979 16.8%

Sales and office occupations 97,055 24.4% 82,940 27.2% 66,284 25.4%

Construction, maintenance, repair 23,590 5.9% 23,271 7.6% 19,647 7.5%

Production & transportation 47,469 12.0% 21,353 7.0% 41,246 15.8%

INDUSTRY

Civilian employed population 16+ 397,038 100.0% 305,207 100.0% 261,426 100.0%

Agriculture & mining 908 0.2% 1,039 0.3% 568 0.2%

Construction 17,756 4.5% 22,258 7.3% 14,434 5.5%

Manufacturing 43,838 11.0% 17,291 5.7% 26,967 10.3%

Wholesale trade 12,844 3.2% 10,645 3.5% 10,631 4.1%

Retail trade 44,637 11.2% 37,731 12.4% 27,185 10.4%

Transportation & utilities 25,204 6.3% 15,633 5.1% 22,488 8.6%

Information 12,367 3.1% 9,003 2.9% 5,763 2.2%

Finance, insurance, & real estate 37,646 9.5% 29,281 9.6% 23,548 9.0%

Professional & administrative 57,240 14.4% 37,747 12.4% 31,704 12.1%

Educational services & health care 88,105 22.2% 72,357 23.7% 57,971 22.2%

Arts, entertain, hotel, food svcs 28,207 7.1% 29,092 9.5% 17,554 6.7%

Other private services 16,957 4.3% 12,045 3.9% 12,570 4.8%

Public administration 11,329 2.9% 11,085 3.6% 10,043 3.8%

INCOME AND BENEFITS

Total households 283,337 100.0% 236,447 100.0% 184,879 100.0%

Less than $10,000 13,282 4.7% 10,619 4.5% 9,929 5.4%

$10,000 to $14,999 8,932 3.2% 7,727 3.3% 7,480 4.0%

$15,000 to $24,999 19,568 6.9% 15,035 6.4% 15,981 8.6%

$25,000 to $34,999 20,842 7.4% 16,566 7.0% 15,930 8.6%

$35,000 to $49,999 28,252 10.0% 22,821 9.7% 22,363 12.1%

$50,000 to $74,999 46,448 16.4% 36,325 15.4% 31,245 16.9%

$75,000 to $99,999 41,674 14.7% 30,201 12.8% 23,606 12.8%

$100,000 to $149,999 52,347 18.5% 43,557 18.4% 29,003 15.7%

$150,000 to $199,999 27,636 9.8% 25,989 11.0% 12,140 6.6%

$200,000 or more 24,356 8.6% 27,607 11.7% 17,202 9.3%

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Median household income (dollars) 77,478 154.8% 81,308 162.5% 65,876 131.6%

Mean household income (dollars) 95,302 139.6% 108,046 158.3% 92,592 135.6%

Families 204,087 100.0% 165,144 100.0% 131,219 100.0%

Less than $10,000 6,443 3.2% 4,242 2.6% 5,293 4.0%

$10,000 to $14,999 4,075 2.0% 2,320 1.4% 3,314 2.5%

$15,000 to $24,999 8,698 4.3% 5,727 3.5% 8,707 6.6%

$25,000 to $34,999 12,114 5.9% 9,130 5.5% 8,804 6.7%

$35,000 to $49,999 17,568 8.6% 12,542 7.6% 13,850 10.6%

$50,000 to $74,999 33,211 16.3% 23,795 14.4% 21,829 16.6%

$75,000 to $99,999 31,827 15.6% 22,658 13.7% 16,792 12.8%

$100,000 to $149,999 44,484 21.8% 36,820 22.3% 25,401 19.4%

$150,000 to $199,999 24,163 11.8% 23,288 14.1% 11,251 8.6%

$200,000 or more 21,504 10.5% 24,622 14.9% 15,978 12.2%

Median family income (dollars) 89,959 148.4% 101,294 167.1% 80,770 133.3%

Mean family income (dollars) 107,794 135.9% 127,177 160.3% 108,163 136.3%

Per capita income (dollars) 33,367 128.0% 40,824 156.7% 32,854 126.1%

Median earnings for workers 41,047 142.0% 41,474 143.5% 33,414 115.6%

Median earnings for male full-time 66,066 142.1% 72,381 155.7% 51,307 110.3%

Median earnings for female full-time 50,044 136.9% 51,108 139.8% 44,105 120.7%

PERCENTAGE BELOW POVERTY LEVEL

All families 7.3% 64.6% 5.1% 45.1% 9.3% 82.3%

All people 9.9% 64.7% 7.2% 47.1% 10.9% 71.2%

All three of these counties are relatively affluent in the sense that mean and median income levels are well above average, and poverty rates are well below average. Nonetheless, the income levels in Middlesex and Monmouth are well above those of Union County, which still has some pockets of poverty in Elizabeth. In terms of the employment distribution, both Middlesex and Union counties have fairly strong manufacturing sectors, with the proportion of employees near or slightly above the national average. Monmouth County also has an above-average proportion of employees in leisure and entertainment; the other two counties are well below average in that respect. Other components of employment are generally close to the national average proportions.

45

Table 7-12. Labor Market Statistics, 2004-2013, for 7 Northern New Jersey Counties

Labor Force Employed Unemployed Un Rate, %

7 Counties 2004 2,376,423 2,259,618 116,805 4.9

2005 2,390,876 2,285,333 105,543 4.4

2006 2,419,373 2,310,212 109,161 4.5

2007 2,421,251 2,321,479 99,772 4.1

2008 2,447,121 2,318,195 128,926 5.3

2009 2,459,550 2,244,844 214,706 8.7

2010 2,466,969 2,239,152 227,817 9.2

2011 2,469,436 2,248,455 220,981 8.9

2012 2,489,954 2,268,365 221,589 8.9

2013 2,482,101 2,287,048 195,053 7.9

Bergen 2004 462,702 443,247 19,455 4.2

2005 467,206 449,791 17,415 3.7

2006 473,275 455,022 18,253 3.9

2007 472,991 456,594 16,397 3.5

2008 478,529 456,999 21,530 4.5

2009 480,521 443,351 37,170 7.7

2010 475,426 435,664 39,762 8.4

2011 476,982 438,893 38,089 8.0

2012 479,549 441,164 38,385 8.0

2013 477,410 443,734 33,676 7.1

Essex 2004 363,454 340,905 22,549 6.2

2005 361,843 341,544 20,299 5.6

2006 364,175 343,012 21,163 5.8

2007 362,785 343,281 19,504 5.4

2008 365,964 341,818 24,146 6.6

2009 366,994 329,326 37,668 10.3

2010 369,642 328,721 40,921 11.1

2011 368,165 328,299 39,866 10.8

2012 369,077 329,988 39,089 10.6

2013 367,475 332,387 35,088 9.5

Hudson

46

2004 287,381 269,725 17,656 6.1

2005 288,312 272,630 15,682 5.4

2006 290,204 274,266 15,938 5.5

2007 290,990 276,383 14,607 5.0

2008 294,372 275,637 18,735 6.4

2009 299,736 268,408 31,328 10.5

2010 310,323 276,736 33,587 10.8

2011 312,233 280,023 32,210 10.3

2012 316,284 283,879 32,405 10.2

2013 314,272 285,533 28,739 9.1

Morris 2004 265,376 255,660 9,716 3.7

2005 267,813 259,088 8,725 3.3

2006 272,237 263,196 9,041 3.3

2007 272,580 264,282 8,298 3.0

2008 275,553 264,501 11,052 4.0

2009 274,999 255,684 19,315 7.0

2010 272,451 252,478 19,973 7.3

2011 272,313 253,159 19,154 7.0

2012 274,790 255,168 19,622 7.1

2013 274,179 257,024 17,155 6.3

Middlesex 2004 410464 391663 18801 4.6

2005 415943 398420 17523 4.2

2006 421868 403617 18251 4.3

2007 421754 405387 16367 3.9

2008 425867 404422 21445 5.0

2009 427237 391116 36121 8.5

2010 435643 397681 37962 8.7

2011 436170 399472 36698 8.4

2012 442912 406106 36806 8.3

2013 443825 410919 32906 7.4

Monmouth 2004 322012 307448 14564 4.5

2005 324105 310869 13236 4.1

2006 329093 315612 13481 4.1

2007 332191 319687 12504 3.8

2008 335314 318942 16372 4.9

2009 336439 308605 27834 8.3

47

2010 328877 299848 29029 8.8

2011 328301 299891 28410 8.7

2012 329872 300820 29052 8.8

2013 328905 304385 24520 7.5

Union 2004 265,034 250,970 14,064 5.3

2005 265,654 252,991 12,663 4.8

2006 268,521 255,487 13,034 4.9

2007 267,960 255,865 12,095 4.5

2008 271,522 255,876 15,646 5.8

2009 273,624 248,354 25,270 9.2

2010 274,607 248,024 26,583 9.7

2011 275,272 248,718 26,554 9.6

2012 277,470 251,240 26,230 9.5

2013 276,035 253,066 22,969 8.3

On balance, the unemployment rate for these 7 counties in Northern New Jersey is close to the national average, but there are significant differences among individual counties. Essex (Newark), Hudson (Jersey City), and Union (Elizabeth) have unemployment rates well above average, with rates of 9.5%, 9.1%, and 8.3% respectively in 2013. On the other hand, the unemployment rates in Bergen and Morris Counties were 7.1% and 6.3%, compared to 7.4% nationally. For 2012, the unemployment rate for this 7-county group was unchanged, slightly better than its New York counterpart but far weaker than the decline in the national average; however, in 2013, similarly to the rest of the New York City area, the unemployment rate dropped sharply.

48

Table 7-13. Level and Growth of Population, 7 Northern New Jersey Counties

Bergen Essex Hudson Morris Middlesex Monmouth Union 7 county

2011 911,004 785,137 641,224 494,976 816,618 630,092 539,494 4,818,545

2010 906,184 784,099 634,979 492,681 811,266 630,821 537,475 4,797,505

2009 900,319 781,943 628,572 490,779 805,204 628,669 532,434 4,767,920

2008 895,328 778,165 619,533 489,743 799,191 627,348 527,528 4,736,836

2007 890,817 778,996 613,637 488,355 792,137 626,644 524,960 4,715,546

2006 889,406 781,027 613,577 487,486 786,890 626,934 525,153 4,710,473

2005 891,446 786,341 614,664 485,472 787,329 627,838 526,161 4,719,251

2004 893,378 791,305 614,607 483,997 781,582 628,605 526,916 4,720,390

2003 892,214 795,167 614,813 481,000 775,973 627,413 527,611 4,714,191

2002 890,647 795,625 615,554 477,234 769,280 624,532 527,625 4,700,497

2011/10 0.53% 0.13% 0.98% 0.47% 0.66% -0.12% 0.38% 0.44%

2010/09 0.65% 0.28% 1.02% 0.39% 0.75% 0.34% 0.95% 0.62%

2009/08 0.56% 0.49% 1.46% 0.21% 0.75% 0.21% 0.93% 0.66%

2008/07 0.51% -0.11% 0.96% 0.28% 0.89% 0.11% 0.49% 0.45%

2007/06 0.16% -0.26% 0.01% 0.18% 0.67% -0.05% -0.04% 0.11%

2006/05 -0.23% -0.68% -0.18% 0.41% -0.06% -0.14% -0.19% -0.19%

2005/04 -0.22% -0.63% 0.01% 0.30% 0.74% -0.12% -0.14% -0.02%

2004/03 0.13% -0.49% -0.03% 0.62% 0.72% 0.19% -0.13% 0.13%

2003/02 0.18% -0.06% -0.12% 0.79% 0.87% 0.46% 0.00% 0.29%

2011/02 0.25% -0.15% 0.45% 0.41% 0.17% 0.10% 0.25% 0.28%

In terms of population growth, this is a sluggish section of both the overall economy and the NYC metropolitan area, with average annual growth of less than 0.3%. Even so, the population growth follows the pattern of the rest of the metro area; a decline in the first half of the decade, followed by a significant improvement in the second half. However, Essex County failed to post any increase in population during the decade; growth in the areas bordering New York occurred primarily in Hudson County, as the financial sector moved across the Hudson River to Jersey City.

Table 7-14. Level and Growth of Personal Income (Billion $), 7 Northern New Jersey Counties

Bergen Essex Hudson Morris Middlesex Monmouth Union Total

2011 60.21 41.58 30.38 35.50 41.11 37.60 27.98 274.35

2010 57.44 40.01 28.75 34.18 38.69 36.12 26.63 261.81

2009 56.36 37.98 26.82 32.98 38.22 35.73 25.76 253.85

2008 61.09 40.20 26.57 36.25 39.19 37.06 27.59 267.95

2007 60.04 38.83 24.21 34.77 37.56 36.58 26.64 258.63

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2006 55.78 36.93 22.69 33.11 35.28 34.29 25.54 243.63

2005 50.55 33.99 21.15 30.55 32.87 31.71 23.28 224.10

2004 48.66 32.77 19.99 29.42 31.66 30.50 22.54 215.54

2003 45.62 30.81 19.24 27.32 30.69 28.45 21.87 204.00

2002 46.24 30.14 19.00 26.93 30.04 27.85 21.51 201.71

2011/10 4.82% 3.92% 5.67% 3.86% 6.25% 4.10% 5.07% 4.79%

2010/09 1.92% 5.34% 7.20% 3.64% 1.22% 1.09% 3.38% 3.14%

2009/08 -7.74% -5.52% 0.94% -9.02% -2.47% -3.61% -6.63% -5.26%

2008/07 1.75% 3.53% 9.75% 4.26% 4.33% 1.33% 3.57% 3.60%

2007/06 7.64% 5.14% 6.70% 5.01% 6.46% 6.66% 4.31% 6.16%

2006/05 10.35% 8.65% 7.28% 8.38% 7.33% 8.15% 9.71% 8.71%

2005/04 3.88% 3.72% 5.80% 3.84% 3.84% 3.94% 3.28% 3.97%

2004/03 6.66% 6.36% 3.90% 7.69% 3.16% 7.23% 3.06% 5.66%

2003/02 -1.34% 2.22% 1.26% 1.45% 2.17% 2.15% 1.67% 1.13%

2011/02 2.98% 3.63% 5.35% 3.12% 3.49% 3.39% 2.96% 3.47%

The growth in income for these 7 counties was well below the national and metropolitan average. The more rapid growth in population in Hudson County was also reflected in the growth in income, due to the expansion of jobs in the financial services area. In this respect we note that the financial market swoon in late 2008 did not hurt Hudson County, as the move toward increasing the financial presence outside of lower Manhattan continued in spite of the temporary setback.

D. Commuting Patterns for Bronx County To conclude this section, we turn to commuting patterns for the Bronx County. In determining the economic impact of new job creation, it is necessary to choose the counties that form the relevant area for analysis. The commuting patterns of the workforce data from 2006-2010 provided by the American Community Survey are used to determine the optimal mix of counties to be included in the multiplier calculations. These commuters spend most of their paychecks in the counties where they live, so the economic impact of the Project creates some new induced jobs in bordering counties. Also, some of the goods and services purchased by the new businesses are produced or purchased from establishments in neighboring counties.

As shown below in Table 7-18, the total workforce in Bronx County for the period

from 2006 to 2010 period was 359,637. The 16 counties in the Study Region represent 96.2% of the total workforce. Therefore, the Study Region is a reasonable and valid geographic area.

50

Table 7-15. Commuting Patterns for Bronx County

County of Residence Workforce

New York Bronx County 226,315

New York Westchester County 29,908

New York New York County 27,179

New York Queens County 21,038

New York Kings County 13,534

New Jersey Bergen County 6,516

New York Nassau County 6,334

New York Rockland 5,681

New York Suffolk County 3,364

New Jersey Hudson County 1,669

New York Richmond County 1,580

New Jersey Essex County 1,208

New Jersey Union County 569

New Jersey Middlesex County 541

New Jersey Morris County 329

New Jersey Monmouth County 303

Total 16 Counties 346,068

Total Bronx 359,637

% in Bronx 96.2

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Resume of Dr. Michael K. Evans

[email protected]

CURRENT AND PREVIOUS POSITIONS

• Chairman, Evans, Carroll & Associates, Inc., 1980-present (previously Evans Economics)

Economic consulting firm specializing in EB-5 immigration analysis, economic impact studies of development projects and new construction, models of state and local tax receipts, impact of current and proposed government legislation, and construction of econometric models for individual industries and companies.

• Chief Economist, American Economics Group, 2000-2008.

Built a comprehensive state modeling system that provides economic analysis for a variety of consulting projects (see below).

• Clinical Professor of Economics, Department of Managerial Economics and Decision Sciences (MEDS), Kellogg Graduate School of Management, Northwestern University, 1996-99.

Taught courses in macroeconomics and business forecasting. Wrote textbooks for both courses.

• Winner of Blue Chip Economic Indicator Award for most accurate macroeconomic forecasts during the past four years, November 1999

• Founder and President, Chase Econometric Associates, 1970-1980

• Assistant and Associate Professor of Economics, Wharton School, University of Pennsylvania, 1964-69.

Co-developer of the original Wharton Model.

• Visiting Professor, Radford University, (Radford, VA), 1987

Chairman of Institute for International Economic Competitiveness

• Visiting Lecturer, Hebrew University (Jerusalem), 1966-67

Built econometric model of the Israeli economy

mailto:[email protected]

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PREVIOUS ACTIVITIES AND EDUCATION

• Ph. D. in Economics, Brown University. Dissertation, "A Postwar Quarterly Model of the United States Economy, 1948-1962". A. B. in Mathematical Economics, Brown University

• Contributing Editor, Industry Week Wrote a column in each issue on economic and financial trends as they impact the manufacturing sector. • Editor, The Evans Report Weekly newsletter discussing economic trends and financial markets. Pioneered the concept of the Monthly Tracking Model to incorporate recent economic releases into the overall economic forecast, including methods to predict these economic data. • Consultant, National Printing Equipment and Supply Association Prepared quarterly forecasts of shipments of printing equipment and graphic arts supplies by product line, based on an econometric model constructed for NPES. Also prepares analysis and forecasts of exports and imports by principal product line. • Consultant, APICS -- The Educational Society for Resource Management, Designed and developed the APICS Business Outlook Index, which used survey data collected by the Evans Group to measure current production, production plans, shipments, employment, new orders, unfilled orders, inventory stocks, and the comparison of the actual to desired inventory/sales ratio to predict short-term changes in manufacturing sector activity. The results of this survey appeared every month in APICS: The Performance Advantage • Consultant, American Hardware Manufacturing Association Wrote a separate weekly edition of the Evans Report analyzing recent trends in the hardware and housing industries, including forecasts of the hardware industry based on an econometric model developed for AHMA. • Board of Economists, Los Angeles Times Wrote column every 6 weeks (5 other economists on the Board) • Columnist, United Press International

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Wrote twice-weekly column, "Dollars and Trends" • Consultant, Senate Finance Committee, Built the first large-scale supply-side model of the U. S. economy • Consultant, Environmental Protection Agency and Council on Environmental Quality Estimated inflationary impact of government regulations • Consultant, National Aeronautics and Space Administration Estimate impact of R&D spending on productivity growth • Consultant, U. S. Treasury Estimated impact of investment tax credit and accelerated depreciation on capital spending by industry • Consultant, U. S. Department of Agriculture Built large-scale econometric model of agricultural sector of U. S. economy

• Consultant, Organization of Economic Cooperation and Development Built econometric model of the French economy

SAMPLE OF RECENT CONSULTING PROJECTS

A. Economic Impact of EB-5 Immigrant Investor Programs and New Markets Tax Credits

For more information on these projects, see www.evanseb5.com

Key to symbols: N, new regional center, E, extension of existing center

http://www.evanseb5.com/

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List is current as of January 31, 2014. There are over 400 projects listed, but while the list is extensive, it is not complete. In cases where the results are very similar, they are

either grouped together or omitted entirely

E ● Calculated the economic impact of construction and operation of boutique hotel in Ontario, CA.

E ● Calculated the economic impact of construction and operation of hotel in mid-town Manhattan.

E ● Calculated the economic impact of renovation and operation of a commercial mixed-use building

in San Francisco.

E ● Calculated the economic impact of construction and operation of luxury condominium building in

downtown Manhattan.

E ● Calculated the economic impact of construction and rental income of a medical facility in Winston-

Salem, NC

E ● Calculated the economic impact of construction and rental income of office building in mid-town

Manhattan.

E ● Calculated the economic impact of construction and rental income of office building in downtown

Cleveland, OH.

E ● Calculated the economic impact of construction and operation of mixed-use commercial and

residential project in Tacoma, WA.

E ● Calculated the economic impact of construction and operation of senior living facility in Chicago.

N ● Calculated the economic impact of construction and operation of manufacturing plant to produce

synthetic coke fuel for steel manufacturing in rural West Virginia.

E ● Calculated the economic impact of construction and operation of apartment building in

Washington, DC.

E ● Calculated the economic impact of construction and operation of medical complex in Flushing,

Queens, NY.

E ● Calculated the economic impact of construction and operation of hotel in Chicago

E ● Calculated the economic impact of construction and operation of insurance plan for pets,

headquartered in New York City.

E ● Calculated the economic impact of construction and operation of hotel in the Gowanus district of

Brooklyn.

E ● Calculated the economic impact of construction and operation of hotel near Times Square,

Manhattan

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E ● Calculated the economic impact of construction and operation of 3 senior living and residential

facilities in Southeast FL.

E ● Calculated the economic impact of construction and operation of senior living and residential

facilities in Seattle WA.(3 different projects)

E ● Calculated the economic impact of construction and operation of charter school in Palm Beach

County, FL

E ● Calculated the economic impact of construction of mixed-use commercial project and

infrastructure in New York City.

N ● Calculated the economic impact of construction and operation of pellet mill in Arkansas, used to

make fuel pellets mainly for export to Europe.

E ● Calculated the economic impact of construction of senior living facilities in Houston, Texas

E ● Calculated the economic impact of construction of skilled nursing facility in Las Vegas, NV

E ● Calculated the economic impact of construction and operation of hotel in Chicago

E ● Calculated the economic impact of construction and operation of wholesale distribution center

and retail outlets in Queens, NYC

E ● Calculated the economic impact of construction and operation of New Quincy Market in Quincy,

MA.

E ● Calculated the economic impact of operation of fund for providing capital for production of films in

New Orleans, LA

E ● Calculated the economic impact of renovation and operation of mixed-use facilities and rebuilding

of infrastructure in Harlem, Manhattan, NYC

E ● Calculated the economic impact of construction and operation of mixed-use residential and

commercial buildings in New York City (2 projects)

N ● Calculated the economic impact of construction and operation of sports complex in Attleboro, MA.

E ● Calculated the economic impact of construction and operation of sports stadium and related retail

ventures in Las Vegas, NV

E ● Calculated the economic impact of construction and operation of resort complex in Hawaii

E ● Calculated the economic impact of construction and operation of mixed-use commercial and

residential property in Emerald Falls, OK

E ● Calculated the economic impact of construction and operation of five hotels in rural Texas

N ● Calculated the economic impact of developing and operating oil wells in the Bakken Formation in

North Dakota.

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E ● Calculated the economic impact of construction and operation of 4 mixed-use buildings in the New

York City metropolitan area.

E ● Calculated the economic impact of construction and operation of a hotel in Queens, NYC

E ● Calculated the economic impact of constructing and operating a hotel near LaGuardia airport, New York E ● Calculated the economic impact of constructing and operating a restaurant and wine bar on the Las Vegas strip. E ● Calculated the economic impact of constructing a medical complex in the Bronx, NY E ● Calculated the economic impact of constructing and operating a theme park restaurant in Downtown Disney World, Orlando, FL E ● Calculated the economic impact of constructing and operating an oil refinery in the Houston, TX metropolitan area N ● Calculated the economic impact of developing a planned town with single and multi-family residences, commercial space, and solar energy on a ranch in Hendry County, FL E ● Calculated the economic impact of developing a luxury condominium in Miami, FL E ● Calculated the economic impact of constructing and operating a hotel near Times Square, New York City E ● Calculated the economic impact of constructing and operating a hotel in Pascagoula, MS E ● Calculated the economic impact of constructing and operating a hotel in Orlando, FL E ● Calculated the economic impact of constructing and operating a senior living facility in suburban Atlanta, GA E ● Calculated the economic impact of expansion of commercial facilities in Cleveland, OH in (a) the area around University Circle, and (b) the downtown Flats area. N ● Calculated the economic impact of constructing and operating a geothermal power plant in Oregon. E ● Calculated the economic impact of constructing and operating a luxury apartment building in downtown Manhattan E ● Calculated the economic impact of constructing and operating a senior living facility in Palm Beach County, FL E ● Calculated the economic impact of constructing several multi-family residential buildings in Texas (3 separate projects)

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E ● Calculated the economic impact of operating a home insurance company to relieve the burden of Citizens Insurance in the State of Florida. E ● Calculated the economic impact of constructing and operating a restaurant chain specializing in high-quality health foods, Palm Beach County, FL E ● Calculated the economic impact of constructing and operating multi-family residential properties, hotels, and senior living facilities in the Denver metropolitan area. E ● Calculated the economic impact of constructing and operating multi-family residential properties, hotels, and senior living facilities in the Atlanta metropolitan area. E ● Calculated the economic impact of constructing and operating multi-family residential properties, hotels, and senior living facilities in the Atlanta metropolitan area. E ● Calculated the economic impact of constructing and operating multi-family residential properties, hotels, and senior living facilities in the Miami metropolitan area. E ● Calculated the economic impact of producing a series of 10 major motion pictures ($100 million or more each) in the New York City area. E ● Calculated the economic impact of constructing luxury homes on Key Largo, FL N ● Calculated the economic impact of construction and operation of three projects in Puerto Rico: a hotel in San Juan, a condo/hotel village in Arecibo, and a power plant in Loiza. Used Puerto Rico input/output model updated by ECA. N ● Calculated the economic impact of construction and operation of a time-sharing condominium in Hawaii. E ● Calculated the economic impact of constructing and operating a large Ferris Wheel on the Las Vegas strip, including the impact of advertising revenues and ancillary retail space. E ● Calculated the impact of operating an insurance company in South Florida. N ● Calculated the economic impact of constructing and operating a medical complex in the Houston, TX metropolitan area. N ● Calculated the economic impact of constructing a new interchange for the Pennsylvania Turnpike and I-95. E ● Calculated the economic impact of constructing and operating a mixed-use commercial facility in Newark, NJ E ● Calculated the economic impact of improving the infrastructure at the waterfront in Oakland, CA E ● Calculated the economic impact of constructing and operating a hotel in San Diego CA.

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E ● Calculated the economic impact of constructing and operating a hotel in downtown Cleveland, OH E ● Calculated the economic impact of constructing three multi-family residential properties in Austin, TX. E ● Calculated the economic impact of renovating and operating the former Wilshire hotel in Los Angeles. E ● Calculated the economic impact of constructing and operating a luxury hotel in Austin, TX E ● Calculated the economic impact of constructing a mixed-use industrial facility in Pflugerville, TX E ● Calculated the economic impact of constructing and operating charter schools in several different locations in Florida, and in Chicago (5 separate projects). N ● Calculated the economic impact of constructing and operating a manufacturing plant for wood pellets used for heating in Southern Georgia E ● Calculated the economic impact of constructing and operating a wind farm in the Texas panhandle. E ● Calculated the economic impact of constructing several luxury apartment buildings and hotels in Manhattan (4 separate projects) E ● Calculated the economic impact of operating a steel distribution center in Palm Beach County, FL N ● Calculated the economic impact of operating a boat for cleaning and processing fish anchored off the Mississippi River in Kentucky. E ● Calculated the economic impact of constructing and operating hotels in Seattle, WA (2 separate projects) N ● Calculated the economic impact of operation of a facility for bio-science trials, Newark, NJ N ● Calculated the economic impact of building and operating a steel mill in Northeast Arkansas. E ● Calculated the economic impact of drilling and extracting oil, natural gas, and natural gas liquids, Oklahoma (2 projects) E ● Calculated the economic impact of expanding a golf and ski resort, and a furniture manufacturing plant, in Northern New Hampshire E ● Calculated the economic impact of constructing and operating a medical facility and student dormitory in Brooklyn, NY N ● Calculated the economic impact of oil drilling and extraction in Marion County, TX

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E ● Calculated the economic impact of developing and operating mixed-use facilities in Los Angeles N ● Updated an input/output model for Puerto Rico, and used this model to determine the economic impact of constructing and operating a resort in Boqueron Bay E ● Calculated the economic impact of renovating properties for Mississippi State University and adding a hotel E ● Calculated the economic impact of renovating an assisted living facility in Anniston, AL E ● Calculated the economic impact of constructing an apartment tower in Phoenix, AZ E ● Calculated the economic impact of constructing and operating a hotel in Dallas, TX. Also calculated the impact of two assisted living centers in Dallas. N ● Calculated the economic impact of a mixed-use commercial facility in New London, CT. N ● Calculated the economic impact of a mixed-use commercial facility in suburban Chicago, IL ● Calculated the economic impact of a hotel, casino, and commercial mixed-use properties on the island of Matsu, Taiwan Republic (not an EB-5 project but similar methodology was used) N ● Calculated the economic impact of extracting lithium compounds from the Salton Sea in Imperial County, CA N ● Calculated the economic impact of constructing and operating geothermal power plants in southern CA E ● Calculated the economic impact of constructing luxury hotels and condominiums in Manhattan (6 separate projects) N ● Calculated the economic impact of producing motion pictures and TV programs in Miami, FL E ● Calculated the economic impact of constructing and operating a hotel, shopping center, and residences in Boca Raton, FL

E ● Calculated the economic impact of developing and operating a time-sharing resort on Lake Tahoe, CA N ● Calculated the economic impact of a series of child day care and learning centers in San Antonio and Austin, TX N ● Calculated the economic impact of a mixed-use commercial and cultural center in Chinatown, Philadelphia E ● Calculated the economic impact of developing and operating charter schools in Florida (4 projects) N ● Calculated the economic impact of developing the Boston Seaport project near the Boston Harbor.

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E ● Calculated the economic impact of a mixed-use hotel and commercial project in downtown Boston, MA E ● Calculated the economic impact of expanding the Hialeah racetrack, Hialeah, FL E ● Calculated the economic impact of developing and expanding a resort area in Benton Harbor, MI N ● Calculated the economic impact of developing and operating a Holiday Inn near the World Trade Center, Manhattan N ● Calculated the economic impact of developing and operating a Marriott Courtyard hotel in downtown Houston. N ● Calculated the economic impact of building a greenhouse in Central California. N. Calculated the economic impact of developing an aircraft manufacturing plant in Northeast Arkansas. N. Calculated the economic impact of developing and operating alternative fuels plant in Clark County, NV. N ● Calculated the economic impact of a destination winery and associated attractions in North Carolina. E ● Calculated the economic impact of building and operating a luxury hotel in Palm Beach, FL N ● Calculated the economic impact of operating a Kosher cheese plant in upstate New York E ● Calculated the economic impact of developing and operating a hotel in Chicago, IL N ● Calculated the economic impact of expanding the operations of a plumbing and HVAC contractor E ● Calculated the economic impact for 4 separate projects in Guam, based on the input/output model previously developed by ECA E ● Calculated the economic impact of a resort in the Commonwealth of the Northern Mariana Islands, based on the input/output model previously developed by ECA. E ● Calculated the economic impact of several mixed-use commercial projects in Southern California (4 such projects, each one covering 4 to 6 counties, including Clark County, NV) E ● Calculated the economic impact of a hotel in Norwalk, CT E ● Calculated the economic impact of copper mines throughout the state of Arizona E ● Calculated the economic impact of water park and hotel in Arlington Heights, IL (suburban Chicago)

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N ● Calculated the economic impact of renovation and expansion of Las Vegas casino (3 separate projects) N ● Calculated the economic impact of construction and operation of mixed-use shopping and commercial center in Hollywood Park, FL N ● Calculated the economic impact of development of office building in South Union Lake region of Seattle, WA E ● Calculated the economic impact of Development of mixed-use commercial and residential building in downtown Seattle, WA N. ● Calculated the economic impact of commercial mixed-use projects in New York City, upstate New York, and Northern New Jersey (one project) N ● Calculated the economic impact of developing and operating a major amusement park complex (rival to Disney World) near Lake Okeechobee, FL N●. Calculated the economic impact of constructing and operating a hotel and conference center in Toledo, Ohio. N. ● Calculated the economic impact of renovating and expanding the New York Military Academy in Newburgh, NY N ● Calculated the economic impact of developing a mixed-use commercial project in downtown Philadelphia, PA (2 separate projects) N ● Calculated the economic impact of a film studio to produce motion pictures and TV programs in Los Angeles, CA. N ● Calculated the economic impact of building student housing in Arlington, TX N ● Calculated the economic impact of developing and operating a manufacturing plant for sports medical devices in suburban Chicago, IL N ● Calculated the economic impact of developing natural gas wells and wind farm in the Pocono Mountains section of Pennsylvania. N ● Calculated the economic impact of an assisted living center, hotel, and water park in Eastern CT. N ● Calculated the economic impact of producing movies in New Mexico N ● Calculated the economic impact of developing and operating a chain of child learning centers in Houston, TX

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N ● Calculated the economic impact of developing and operating a chain of medical research and supply centers in Houston. N ● Calculated the economic impact of developing and operating a chain of frozen yogurt stores in a wide area along the Gulf of Mexico, including locations in Florida, Alabama, Mississippi, Louisiana, and Texas N ● Calculated the economic impact of developing and operating assisted living centers and ancillary activities for several locations in Northeast Florida. N ● Calculated the economic impact of the construction and operation of an assisted living center in Santa Ana, CA N ● Calculated the economic impact of the construction and operation of several BBQ restaurants in South Florida. N ● Calculated the economic impact of the drilling oil wells in 8 counties in Texas and Louisiana. N ● Calculated the economic impact of operating coal mines for metallurgical coal in West Virginia. N ● Calculated the economic impact of operating gold mines in Alaska. N ● Calculated the economic impact of constructing and operating a mixed-use commercial center in Flushing, NY N ● Calculated the economic impact of constructing and operating two hotels, one in downtown San Diego, and one in Escondido, CA N ● Calculated the economic impact of expanding and operating an auto racing track in Palm Beach, FL N● Calculated the economic impact of building and operating mobile housing villages for disaster relief. N● Calculated the economic impact of operating an “incubator” for research on medical devices, preparations, and services in Houston, TX. N● Calculated the economic impact of constructing and operating a mixed-use commercial center in Denver, CO. N● Calculated the economic impact of constructing and operating a charter school in Miami/Dade County, FL E● Calculated the economic impact of constructing and operating a hotel in Manhattan, NY N● Calculated the economic impact of constructing and operating hotels, assisted living centers, and mixed-use commercial buildings in 8 counties in Southern California

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N● Calculated the economic impact of constructing and operating a charter school in Broward County, FL N● Calculated the economic impact of renovating a former public housing project in Chicago, IL N● Calculated the economic impact of starting a high-tech company for optical displays in Orlando and Gainesville, FL N● Calculated the economic impact of constructing and operating luxury hotels in four Southern California counties E● Calculated the economic impact of expanding a manufacturing company in Ann Arbor, MI N● Calculated the economic impact of reconverting an old mill building into offices and other commercial uses in Bristol County, MA N● Calculated the economic impact of a film and TV production studio in Los Angeles, CA N● Calculated the economic impact of constructing and operating various residential and commercial buildings in 35 Texas counties. N● Calculated the economic impact of constructing and operating the world’s tallest residential structure in Chicago, IL N● Calculated the economic impact of constructing and operating a mixed-use commercial and residential building in Seattle, WA N● Calculated the economic impact of constructing and operating a hotel in Cleveland, OH N● Calculated the economic impact of a research facility in Jupiter, FL N● Calculated the economic impact of constructing and operating an assisted living center in Horry County, SC N● Calculated the economic impact of constructing and operating a chain pharmacy in Chicago, IL E● Calculated the economic impact of constructing and operating a high-end hotel and resort in Aspen, CO N● Calculated the economic impact of constructing and operating an assisted living center in Dallas, TX E● Calculated the economic impact of constructing and operating an medical assistance company in Bronx, NY E● Calculated the economic impact of constructing and operating a mixed-use commercial building in Queens, NY

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E● Calculated the economic impact of operating a livery service in Queens, NY N● Calculated the economic impact of constructing and operating residential properties in Southern California N● Calculated the economic impact of operating a film and TV production studio in Los Angeles, CA N● Calculated the economic impact of drilling oil wells in Montana N● Calculated the economic impact of constructing and operating various residential and commercial buildings for 43 counties in Texas E● Calculated the economic impact of constructing and operating a restaurant and dinner theater in Guam N● Constructed an input/output model for the Commonwealth of the Northern Mariana Islands, and used it to calculated the economic impact of constructing and operating a restaurant in Saipan. E● Calculated the economic impact of constructing and operating a new hotel in Miami, FL E● Calculated the economic impact of constructing and operating a resort and wellness center in South Florida N● Calculated the economic impact of expanding and operating a ski resort in Vermont. N● Calculated the economic impact of constructing and operating residential and commercial buildings in 20 counties in South Central Texas N● Calculated the economic impact of constructing and operating a hotel near the Newark, NJ airport E● Calculated the economic impact of constructing and operating a company to process health insurance benefits in South Florida E● Calculated the economic impact of constructing and operating a veterinary hospital in Palm Beach County, FL N● Calculated the economic impact of constructing and operating various residential and commercial buildings for all counties in MA, CT, RI, and NH N● Calculated the economic impact of constructing and operating a residential construction company in Maryland N● Calculated the economic impact of constructing and operating various residential and commercial buildings for the entire state of Oklahoma N● Calculated the economic impact of constructing and operating a company for manufacturing dental implants in Cuyahoga County, OH

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N● Calculated the economic impact of constructing and operating a mixed-use commercial facility in Brooklyn, NY N● Calculated the economic impact of constructing and operating an office building for financial services in downtown Manhattan, NY N● Calculated the economic impact of constructing and operating a mixed-use facility in Southern California N● Calculated the economic impact of constructing and operating a retail shopping center in Tampa, FL N● Calculated the economic impact of constructing and operating a retail shopping center in Tampa, FL N● Calculated the economic impact of constructing and operating a mixed-use commercial building in Seattle, WA N● Calculated the economic impact of constructing and operating a charter school in Arizona N● Calculated the economic impact of constructing and operating a resort in northeastern Utah N● Calculated the economic impact of operating an online video game company N● Calculated the economic impact of constructing and operating a hotel in New York City N● Calculated the economic impact of constructing and operating a fashion mall in South Florida E● Calculated the economic impact of construction and operation of a new automobile assembly plant in Petersburg, VA N● Calculated the economic impact of operating a call center for the U.S. government in Muskogee, OK N● Calculated the economic impact of developing a mixed-use commercial and residential center in Scottsdale, AZ N● Calculated the economic impact of constructing and operating a “Green Box” facility in New Jersey to process waste material on a pollution-free basis. N● Calculated the economic impact of constructing and operating a “Green Box” facility in Washington State to process waste material on a pollution-free basis. E● Calculated the economic impact of constructing and operating a new hotel in Coral Gables, FL E● Calculated the economic impact of developing a new residential community in Brevard County, and retail stores and restaurants in St. Lucie County, FL N ● Calculated the economic impact of a new business to store and process field crops in Madison, MS

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N● Calculated the economic impact of operating food service establishments and assisted living centers in 40 counties in Texas. E● Calculated the economic impact of developing a mixed-use commercial center in Miami, FL N● Calculated the economic impact of renovating a theater in New York City to show film highlights of previous Broadway hits. N● Calculated the economic impact of renovating and operating distressed buildings in the San Francisco Bay area. E● Calculated the economic impact of a mixed-use commercial center in Montgomery County, TX E● Calculated the economic impact of expanding a manufacturing facility to produce more energy-efficient lighting in Sarasota, FL N● Calculated the economic impact of developing facilities for amateur sporting events in northern GA N● Calculated the economic impact of developing a mixed-use commercial center in Missoula, MT N● Calculated the economic impact of operating call centers in Las Vegas, NV, and other western Nevada counties E● Calculated the economic impact of constructing and operating a proton cancer treatment center in Boca Raton, FL E● Calculated the economic impact of constructing and operating a “Green Box” facility in Detroit to process waste material on a pollution-free basis. E● Calculated the economic impact of renovating and expanding commercial property in Lower Manhattan N● Calculated the economic impact of constructing student housing and retail stores in Davie, FL E● Calculated the economic impact of constructing residential housing near Harvard University E● Calculated the economic impact of developing mixed-use commercial centers in Broward County, FL E● Calculated the economic impact of renovating a Dallas apartment building E● Calculated the economic impact of renovating and operating a nursing home in Las Vegas, NV E● Calculated the economic impact of constructing a hotel and shopping center in Miami, FL E● Calculated the economic impact of developing a design center in Miami/Dade county, FL

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E● Calculated the economic impact of developing and operating a chain of children’s playrooms and party facilities in South Florida E● Calculated the economic impact of developing a new stadium for the Nets basketball team, to be located in Brooklyn, NY E● Calculated the economic impact of developing a Marriott hotel in Washington, D.C. E● Calculated the economic impact of developing and operating a casino for foreign patrons in Las Vegas, NV E● Calculated the economic impact of operating a series of yogurt fast-food restaurants in South Florida E● Calculated the economic impact of constructing steel homes and commercial buildings in South Florida N● Calculated the economic impact of construction and operation of a farm distillery in Vermont N● Calculated the economic impact of purchase and renovation of deeply discounted residential properties in South Florida N● Calculated the economic impact of a hotel to be built near LaGuardia Airport in Queens, NY N● Calculated the economic impact for several mixed-use commercial and residential properties for a regional center covering southern Wisconsin and northern Illinois. N● Calculated the economic impact for mixed-use commercial project in Flushing, NY E● Calculated the economic impact for major new hotel near the Washington, D. C. conference center N● Calculated the economic impact of an assisted living center in suburban Atlanta, GA N● Calculated the economic impact of an office tower in mid-town Manhattan for the diamond trade N● Calculated the economic impact of three mixed-use commercial and residential projects in Santa Clara County, CA N● Calculated the economic impact of six mixed-use commercial and residential projects in Los Angeles, Orange, Riverside, and San Bernardino counties N● Calculated the economic impact of operating a chain of pizza restaurants in southern Florida. N● Calculated the economic impact of constructing and operating an assisted living facility in Atlanta, GA

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E● Calculated the economic impact of constructing and operating an expansion of University Hospital in Cleveland, OH E● Calculated the economic impact of a wastewater treatment plant in Victorville, CA N● Calculated the economic impact of drilling for geothermal energy and constructing and operating power plants in several counties in Nevada E● Calculated the economic impact of a vacation club operation in Orlando, FL E● Calculated the economic impact of constructing and operating an extended-stay hotel in Boston, MA E● Calculated the economic impact of constructing and operating an assisted living facility in Walton County, FL N● Calculated the economic impact of manufacturing and constructing residential and commercial steel modular buildings in Lee County, FL E● Calculated the economic impact of a chain of yogurt and juice stores and restaurants in southern Florida E● Calculated the economic impact of two mixed-use commercial developments in Orange County, CA. E● Calculated a Targeted Employment Area by census tracts for six counties in the Houston, TX metropolitan area E● Calculated the expansion of new hybrid car manufacturing facility from Mississippi to Tennessee and Virginia. E● Calculated the economic impact of construction and operation of a skilled nursing facility in Las Vegas, NV. N● Calculated the economic impact of construction and operation of a proton cancer treatment center and medical offices buildings in Los Angeles County, CA. E● Determined the economic impact of improving facilities at the Port of Baltimore in order to attract more shipping from the Panama Canal when the locks are widened. N● Calculated the economic impact of a major hotel and resort area in Ft. Lauderdale, FL. N● Calculated the economic impact of building steel homes in South Florida, including the local manufacture of steel fabricated parts. E● Calculated the economic impact of constructing and operating a hotel at Times Square in New York City. N● Calculated the economic impact of a mixed-used residential and commercial project in Atlanta, GA.

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E● Calculated the economic impact of expanding and opening new restaurants in Dallas, TX. In a separate project, calculated the economic impact of renovating, refurbishing, and operating a boutique hotel in Dallas, TX. E● Calculated the economic impact of building and operating low-income housing in Boston, MA. N● Calculated the economic impact of constructing and operating assisted living facilities in eight rural Texas counties. N● Calculated the economic impact of a mixed-use commercial project in Riverside County, CA. E● Calculated the economic impact of opening a manufacturing plant for “green” motor vehicles in the Detroit, MI area. E● Calculated the economic impact of constructing and operating hotels and restaurants in Columbus, MS. E● Calculated the economic impact of operating restaurants in the Hotel W in Hollywood, CA. N● Calculated the economic impact of a mixed-use commercial project in McCook, IL (suburban Chicago). N● Calculated the economic impact of constructing and operating a water-based amusement facility in San Diego, CA. N● Calculated the economic impact of a mixed-use commercial facility in suburban Cincinnati, OH (project is in KY). E● Calculated the economic impact of constructing and operating a casino, hotel, and restaurant in Las Vegas, NV. N● Calculated the economic impact of a new academic institution for alternative energy in Santa Clarita, CA. N● Calculated the economic impact of several mixed-used projects in San Francisco, Alameda County, Santa Clara County, and Fresno County. N● Calculated the economic impact of a super energy store and solar farm in Riverside County, CA. N● Calculated the economic impact of a prostate cancer treatment center in South Carolina. E● Calculated the economic impact of refurbishing and expanding retail space at the George Washington Bridge in New York City. E● Calculated the economic impact of building Atlantic Yards, new stadium for the New York Nets, in Brooklyn, NY

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N● Calculated the economic impact of an assisted living center and several mixed-use commercial facilities in the Reno, NV area. E● Calculated the economic impact of buying residential properties at deep discount prices, refurbishing and selling them, in South Florida. N• Calculated the economic impact for a fractional-ownership marina in Port Charlotte, FL, plus office space, retail stores, restaurants, and a home brokerage office. N• Calculated the economic impact of construction and operation of four retirement homes in Vermont. E• Calculated the economic impact of an upscale retail shopping center in Vail, CO. and a medical office building in Edwards, CO (both in Eagle County). E• Calculated economic impact of a wind turbine manufacturing plant in Larimer County, CO N• Calculated economic impact of a hotel, retail stores, restaurants, office buildings, and bank facilities in Pasadena, CA N• Calculated economic impact of a luxury hotel and condominiums in Destin, FL N• Calculated economic impact of constructing and operating a mixed-use commercial project in Jupiter, FL E• Determined whether 17 possible restaurant locations in Miami/Dade and Broward Counties qualified as Targeted Employment Areas. E• Determined the economic impact of opening and operating a slot-machine casino in Hanover, MD, as part of a proposed EB-5 regional center for the Baltimore metropolitan area. N• Calculated the economic impact of renovating and expanding a restaurant on Martha’s Vineyard, MA, as part of an EB-5 regional center in that state. N• Determined the economic impact of assembling and installing solar panels for residences in the state of LA. E• Determined a Targeted Employment Area for Dallas, TX as part of a proposed EB-5 regional center for the Dallas area. N• Calculated the economic impact for various mixed used projects for a proposed regional center for the entire State of Texas, including shopping centers, office buildings, restaurants, assisted living centers, medical technology facilities, and other personal and business services. N• Calculated the economic impact for the construction and operation of several fast-food restaurants in 10 counties in central California.

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N• Calculated the economic impact for the renovation and expansion of a shopping mall in Greenville, SC. E• Calculated the economic impact of buying existing apartment buildings at deep discount prices, renovating and operating them, in 21 counties in FL. N• Calculated the economic impact of building and operating an institute for proton cancer therapy for a proposed EB-5 regional center in Brooklyn, NY. N• Calculated the economic impact of building and operating a mixed-use facility with medical offices, hotels, and apartments for a proposed EB-5 regional center in Queens, NY. E• Determined a Targeted Employment Area for Philadelphia, PA as part of a proposed EB-5 regional center for the Philadelphia area. N• Calculated the economic impact of a proposed office building and mixed-use facility for an EB-5 regional center in Dallas, Texas N• Calculated the economic impact for various mixed-use projects for a proposed EB-5 regional center in the greater New York City area, including an extended stay hotel, urgent care center, financial lending firm for alternative assets, retail stores, apartments, office space, warehouses, industrial “flex” space, entertainment centers, restaurants, conference and convention centers, nursing home and assisted living facilities, medical offices, medical technology facilities, and high-tech manufacturing. N• Calculated the economic impact of “green” hotels in 10 counties in Central California. N• Calculated the economic impact of generic projects in manufacturing, financial services, health services, hotels, and restaurants for a proposed regional center for the state of Florida. E• Calculated the economic impact of 12 different types of economic activity for an expansion of the Palm Beach Regional Center to five contiguous counties. N• Calculated the economic impact of a new auto parts plant in Alabama to supply parts to Kia automobiles. N• Calculated the economic impact of opening fast-food restaurants in Miami/Dade and Broward counties in FL. N• Calculated the economic impact of a mixed-use commercial center in Flushing, Queens County, NY. E• Calculated the economic impact of revitalizing and renovating part of the Brooklyn Navy Yard for “green” manufacturing facilities. E• Calculated the economic impact of 12 different types of economic activity for various counties in Charlotte and Sarasota counties, FL E• Calculated the economic impact of four new manufacturing and distribution companies in Palm Beach County, FL.

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N• Calculated the economic impact of developing a resort area and building residences in rural Tennessee. N• Calculated the economic impact of developing and operating a resort area in Southern Arizona. N• Calculated the economic impact of revitalizing the depressed East Side of Cleveland, Ohio, with new commercial and industrial buildings. N• Determined the nationwide economic impact of a $1 billion investment in Mississippi for a new hybrid motor vehicle plant. N• Determined the economic impact of expanding a shipyard in Southeastern Louisiana. N• Calculated the economic impact of a new shopping center in Buena Vista, California, and two other generic shopping centers in Los Angeles and San Bernardino counties. E• Calculated the economic impact of enhancing resort areas in eight rural counties in Colorado. N• Calculated the economic impact of the rehabilitation of Fitzsimons Village in Aurora, Colorado, by adding an office building with medical labs, hotel, shopping center, and residences. E• Determined the economic impact of a mixed-use commercial center for the Kansas City metropolitan area. N• Calculated the number of jobs created for a film production company in New York City. N• Calculated economic impact of small-scale rooftop solar panels in various counties in California. N• Calculated economic impact of 7 different types of proposed businesses for a proposed regional center in the Bay Area of California. N• Determined the economic impact of a new biological research park, office building, and logistics center in Wooster, Ohio. E• Calculated the economic effect of a mixed-use urban renewal project in Cleveland, Ohio. N• Calculated economic impact of dairy farm and cheese processing plant in Northern California. N• Determined economic impact of a shipyard, food processing plant, and semiconductor plant for a proposed regional center in Louisiana and Mississippi. N• Calculated the economic impact of a new gaming casino in Natchez, Mississippi. N• Developed an Input/output Model for Guam, which was then used to calculate the economic impact of several generic projects.

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N• Calculated the economic impact of a retail shopping center in suburban Los Angeles County. N• Prepared an economic impact analysis for the “timber to homes” project for a proposed regional center in Colorado. N• Calculated the economic impact for a proposed regional center in Baltimore, Maryland that would include the rebuilding of depressed areas in East Baltimore and along the riverfront. N• Prepared the economic analysis for a proposed EB-5 regional center for the entire state of Florida that included impact calculations for 14 different types of industries. N• Prepared the economic analysis for a proposed EB-5 regional center in the San Francisco Bay area that included calculations for 10 different types of industries. N• Prepared economic impact calculations for proposed EB-5 regional centers in New York City and Northeastern New Jersey. • Calculated the economic impact of a rehabilitated office building in Albuquerque, New Mexico, including the increase in high quality jobs. • Calculated the economic impact of a rehabilitated skilled nursing center in East Los Angeles, California, including the impact on nearby census tracts. N• Calculated the economic impact of development of warehouse and light industrial manufacturing space in Las Vegas, Nevada. N• Calculated the economic impact of rehabilitation and expansion of a vacation and health spa in Sharon Springs, New York N• Calculated economic impact of revitalizing an old resort hotel and adding new facilities for Lake Geneva, WI. • Calculated the employment and tax effects for a portfolio of projects undertaken under the New Market capital program. E• Calculated generic employment changes for proposed EB-5 project for an Inland Port in Palm Beach County, FL N• Calculated the economic impact of construction of El Monte Village in El Monte, CA. • Calculated the economic impact of moving the Social Security Administration building in Birmingham, AL, and revitalizing the surrounding neighborhood. • Calculated the economic impact of rehabbing and expanding the Everett Mall in Everett, WA. • Determined the economic impact of building a new medical center in Charleston, SC

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N• Calculated economic impact of expanding Sugarbush resort in VT. Study included expansion of existing facilities and addition of new facilities. • Calculated economic impact for new market tax credit program in Portsmouth, N.H. Study included both overall economic impact, and the increase in employment and income and the decrease in the unemployment rate and incidence of poverty in individual census tracts. N• Calculated the economic benefits of EB-5 programs for foreign investors for a mixed-use construction project, including a hotel, retail stores, apartments, and a sports stadium in the Washington, D. C. metropolitan area N• Calculated the economic benefits of EB-5 programs for a mixed-used retail shopping center in the New York City metropolitan area. N• Calculated the economic benefits of EB-5 programs for foreign investors for proposed shopping centers in five separate counties in Southern California, including differential impacts of building the shopping centers in different counties.

B. Projects for State and Local Governments

• Constructed an econometric model, using both time-series regression equations and input/output analysis, for the Commonwealth of the Northern Mariana Islands (CNMI). • Constructed an econometric model for the State of New York and determined the change in employment, labor income, and tax revenues for 43 different tax changes proposed by the Governor’s office. • Constructed a detailed econometric model for the State of Pennsylvania to determine the economic impact of the complete panoply of state taxes levied; the model contains over 1,000 equations. In cooperation with American Economics Group, the model was developed to simulate the effect of changes in any state tax rate on households and businesses by income deciles, household status, age of individuals, size of households, and many other demographic variables. The change in business taxes can also be simulated for detailed industry classifications.

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• Determined whether the Washington, D.C. water and sewer authority should accept a high bid for a new waste disposal system. Decision to reject has saved the authority over $200 million, as construction prices turned down sharply as predicted. • Built an econometric model to determine the “tax gap” caused by Internet sales for the state of Minnesota. • Determined appropriate levels of shelter grants individual counties in New York State, and for utility allowances in New York City. Reviewed and prepared testimony in ongoing court cases in these areas. • Calculated the economic impact of the revitalization of downtown Milwaukee, Wisconsin.

C. Economic Impact of Casino Gaming • Built an econometric model to predict the growth of the gaming industry over the next decade, and the economic impact of that industry on employment and tax revenues at the Federal and state levels. • Estimated the economic impact of Indian casino gaming nationally and for the State of Wisconsin. • Determined the economic impact of the Oneida Indian gaming casino on the Green Bay metropolitan area. • Estimated the negative economic impact on the Milwaukee area if a new Indian gaming casino were to be built in Kenosha, Wisconsin.

D. Economic Impact of Smoking Bans and Higher Taxes • Testified on economic impact of smoking bans in Canada; certified as an expert witness by the Court. • Examined the impact of smoking bans on restaurant sales in several different locations in the U.S. to determine how much sales changed when these bans were imposed, and the differential effects depending on whether these bans were partial or total. • Determined the cross-border effects on retail sales from differential rates in cigarette, gasoline, and alcohol excise taxes

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• Determined the economic impact of higher cigarette taxes on minority group employment. • Estimated the economic impact and loss of Federal and state tax revenues when higher cigarette prices lead to increased smuggling.

E. Consulting Projects for Travel and Tourism

• Determined the economic impact of a major casino development on the Island of Matsu, Republic of Taiwan. • Built an econometric model to predict tourism trips and revenues for the major regions of the U.S. economy. • Constructed econometric models to predict tourism in Las Vegas and Orlando. • Using the IMPLAN model, predicted economic impact of tourism and travel expenditures for all counties in Pennsylvania.

F. Other Private Sector Consulting Projects • Determined the beneficial effects on productivity and reduced costs for the Phoenix Mart, which provides a central location for hundreds of small businesses to advertise and market their products and services. • Calculated the revenue gain at the Federal, state and local level generated by domestic manufacturing of Airbus parts and equipment. • Calculated the economic impact of proposed EPA bans on fluoropolymer production. Estimated the size and economic importance of the fluoropolymer industry, and calculated economic impact of shutting down domestic production. • Built an econometric model to examine how U.S. tax and regulatory policies help determine whether the gold mining industry would invest in the U.S. or other countries. Testified before Congress to help defeat legislation inimical to the mining industry. • Built an econometric model to predict consumer bankruptcies, based on recent growth in consumer credit outstanding, the overall economic environment, and recent changes in credit regulations • Estimated the economic impact of the ethanol subsidy on the U.S. economy and Farm Belt States, including the impact on the balance of payments, employment, and tax receipts. Testified before Congress to help pass legislation to extent subsidies to the ethanol industry.

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• Built an econometric model to determine the impact of updating and improving the system of locks on the Upper Mississippi River on corn prices and exports, farm income, and the overall economy.

BOOKS PUBLISHED

Macroeconomics for Managers, Blackwell, 2003 Practical Business Forecasting, Blackwell, 2002 Economic Impact of the Demand for Ethanol, Diane Publishing Company, 1998 How to Make Your Shrinking Salary Support You in Style for the Rest of Your Life, Random House, 1991 The Truth About Supply-Side Economics. Basic Books, 1983. A Supply-Side Model of the U. S. Economy, mimeo (prepared for Senate Finance Committee), 1980. An Econometric Model of the French Economy: A Short-Term Forecasting Model. O.E.C.D, March 1969. Econometric Gaming (with L. R. Klein and M. J. Hartley). Random House, 1969. Macroeconomic Activity: Theory, Forecasting and Control. Harper & Row, 1969. The Wharton Econometric Forecasting Model (with L.R. Klein), Economics Research Unit, Wharton School: University of Pennsylvania Press, 1967. Enlarged edition, 1968. Over 30 articles in major academic journals and publications (list on request)

Economic Impact of The NYC Food Processing and ... · B. Bergen, Essex, Hudson, Middlesex,...

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